Announced in 2016, Gym is an open-source Python library developed to facilitate the development of support knowing algorithms. It aimed to standardize how environments are specified in AI research, making released research study more quickly reproducible [24] [144] while providing users with an easy user interface for interacting with these environments. In 2022, new advancements of Gym have been relocated 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 study generalization. Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro provides the capability 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 at first lack knowledge of how to even stroll, but are given the objectives of discovering to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives find out how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could develop an intelligence "arms race" that could increase an agent's ability to work even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high ability level entirely through experimental algorithms. Before ending up being a team of 5, the first public presentation took place at The International 2017, the annual best champion tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of real time, and that the knowing software was an action in the direction of producing software application that can handle complicated tasks like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement knowing, as the bots discover with time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a full group of 5, and they were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional players, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot gamer shows the obstacles of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated the use of deep reinforcement knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It finds out entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cams to permit the robot to manipulate an arbitrary things by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might resolve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing gradually more tough environments. ADR varies from manual domain randomization by not requiring a human to define randomization ranges. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI models established by OpenAI" to let designers contact it for "any English language AI task". [170] [171]
Text generation

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

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language might obtain world understanding and procedure long-range reliances by pre-training on a with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative variations initially launched to the general public. The full version of GPT-2 was not instantly launched due to issue about prospective misuse, consisting of applications for composing fake news. [174] Some specialists 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 discover "neural phony 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 complete version of the GPT-2 language model. [177] Several sites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, highlighted by GPT-2 attaining cutting edge precision 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 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 allows 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 design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million criteria were likewise trained). [186]
OpenAI specified 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 gave examples of translation and cross-linguistic transfer learning between English and Romanian, and larsaluarna.se in between English and German. [184]
GPT-3 dramatically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the basic capability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the public for concerns of possible abuse, although OpenAI planned to enable 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 specifically 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 personal beta. [194] According to OpenAI, the model can develop working code in over a dozen shows languages, most successfully in Python. [192]
Several concerns with problems, style defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been implicated of discharging copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would terminate 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 top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, examine or generate approximately 25,000 words of text, and write code in all major programming languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and stats about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision criteria, setting 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 variation of GPT-4o replacing 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, start-ups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been developed to take more time to think of their reactions, resulting in greater precision. These designs are particularly effective 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 follower of the o1 thinking model. OpenAI also 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, safety and security researchers had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications providers O2. [215]
Deep research

Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out extensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 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 model that is trained to analyze the semantic resemblance 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 creates images from textual descriptions. [218] DALL-E uses 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 a sad capybara") and generate matching images. It can create pictures of reasonable objects ("a stained-glass window with a picture of a blue strawberry") along with 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 announced DALL-E 2, an upgraded variation of the model with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new primary system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, wiki.whenparked.com a more effective design better able to produce images from intricate descriptions without manual timely engineering and render intricate details like hands and text. [221] It was launched to the general public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can create videos based on brief detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of created videos is unidentified.

Sora's advancement group named it after the Japanese word for "sky", to symbolize its "limitless innovative potential". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos accredited for that purpose, but did not expose the number or the precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it could create videos approximately one minute long. It also shared a technical report highlighting the methods utilized to train the design, and the model's capabilities. [225] It acknowledged some of its shortcomings, including struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they need to have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic 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 revealed his astonishment at the technology's ability to generate realistic video from text descriptions, citing its possible to revolutionize storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause prepare for broadening his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task design that can carry out multilingual speech recognition along with speech translation and raovatonline.org 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 create songs with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to begin fairly but then fall under turmoil the longer it plays. [230] [231] In popular culture, initial 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 bit of lyrics and outputs tune samples. OpenAI mentioned the songs "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" and that "there is a substantial space" between Jukebox and human-generated music. The Verge specified "It's technically impressive, even if the results seem like mushy variations of songs that might feel familiar", while Business Insider mentioned "surprisingly, some of the resulting songs are memorable and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches makers to discuss toy problems in front of a human judge. The function is to research study whether such a method may assist 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 neuron of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an artificial intelligence tool developed on top of GPT-3 that supplies a conversational interface that permits users to ask concerns in natural language. The system then responds with an answer within seconds.