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 released research study more quickly reproducible [24] [144] while offering users with an easy user interface for communicating with these environments. In 2022, brand-new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to resolve single tasks. Gym Retro gives the ability to generalize between games with similar principles but different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first lack understanding of how to even walk, but are provided the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives discover how to adapt to altering conditions. When an agent is then removed from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had actually discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might develop an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high ability level completely through trial-and-error algorithms. Before ending up being a group of 5, the first public demonstration took place at The International 2017, the yearly best champion competition for the video 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 found out by playing against itself for 2 weeks of genuine time, which the learning software application was a step in the instructions of creating software application that can handle intricate tasks like a surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots learn over time by playing against themselves numerous times a day for months, and are rewarded for actions such as killing 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 had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two 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 exhibition match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the challenges of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually demonstrated the usage of deep reinforcement knowing (DRL) agents 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 manipulate physical items. [167] It discovers completely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation issue by utilizing domain randomization, disgaeawiki.info a simulation method which exposes the student to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB video cameras to allow the robot to control an arbitrary things by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot was able to solve 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 toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of creating progressively harder environments. ADR varies from manual domain randomization by not needing a human to define randomization ranges. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing brand-new AI designs developed by OpenAI" to let designers contact it for "any English language AI job". [170] [171]
Text generation

The business has actually 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 design of language might obtain world knowledge and process 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 an unsupervised transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions at first released to the general public. The complete version of GPT-2 was not right away released due to issue about prospective abuse, consisting of applications for writing phony news. [174] Some experts expressed uncertainty that GPT-2 postured a substantial risk.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural fake news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to absolutely 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 model. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose learners, shown by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model 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 problems encoding vocabulary with word tokens by using 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 follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as few as 125 million criteria were also trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 significantly enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly launched to the general public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month complimentary private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has 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 launched in private beta. [194] According to OpenAI, the design can produce working code in over a dozen shows languages, the majority of efficiently in Python. [192]
Several issues with problems, style flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been implicated of discharging copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would terminate assistance for Codex API on March 23, 2023. [198]
GPT-4

On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or bytes-the-dust.com image inputs. [199] They announced that the updated innovation passed a simulated law school bar examination with a score around the top 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 up to 25,000 words of text, and write code in all major programs languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has declined 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 announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision criteria, setting brand-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 launched GPT-4o mini, a smaller sized variation 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 especially beneficial for enterprises, start-ups and developers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been created to take more time to consider their responses, resulting in greater accuracy. These models are particularly reliable in science, coding, and thinking jobs, 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 follower of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecoms O2. [215]
Deep research study

Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform comprehensive web surfing, data analysis, it-viking.ch and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing 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 model that is trained to evaluate the semantic similarity in 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 design 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 a sad capybara") and produce corresponding images. It can create pictures of realistic items ("a stained-glass window with a picture of a blue strawberry") in addition to 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 version of the model with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a 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 model much better able to generate images from intricate descriptions without manual prompt engineering and 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 upon brief detailed prompts [223] as well as extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unidentified.

Sora's development group called it after the Japanese word for "sky", to signify its "endless creative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that function, but did not reveal the number or the precise sources of the videos. [223]
OpenAI demonstrated 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 used to train the design, and the design's capabilities. [225] It acknowledged a few of its drawbacks, including struggles simulating 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 may not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to produce realistic video from text descriptions, mentioning its prospective to reinvent storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause strategies for expanding his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, engel-und-waisen.de 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 recognition as well as 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 create songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned to create 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 genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the songs "reveal local musical coherence [and] follow standard chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge mentioned "It's highly excellent, even if the results seem like mushy variations of songs that might feel familiar", while Business Insider mentioned "surprisingly, some of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, wiki.snooze-hotelsoftware.de which teaches devices to discuss toy issues in front of a human judge. The function is to research study whether such a method may help in auditing AI choices and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of 8 neural network designs which are often studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and various variations 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 permits users to ask concerns in natural language. The system then reacts with a response within seconds.