Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement 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 interface for connecting with these environments. In 2022, brand-new advancements of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to fix single jobs. Gym Retro offers the capability to generalize between games with comparable ideas but different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack understanding of how to even stroll, but are offered the objectives of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the agents find out how to adapt to changing conditions. When an agent is then gotten rid of from this virtual environment and put in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might produce an intelligence "arms race" that could increase a representative's ability to operate 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 find out to play against human gamers at a high ability level totally through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation happened at The International 2017, the yearly best champion competition for the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, larsaluarna.se CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of actual time, and that the learning software was a step in the instructions of creating software application that can manage complex tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement knowing, as the bots discover with time by playing against themselves hundreds of 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, trademarketclassifieds.com the capability of the bots expanded to play together as a complete group of 5, and they were able to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition 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 exhibition match in San Francisco. [163] [164] The bots' final public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot player shows the difficulties of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown using deep support knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It finds out entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation method which exposes the learner 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 electronic cameras to allow the robotic to control an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate 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 producing gradually more challenging environments. ADR varies from manual domain randomization by not requiring a human to specify randomization varieties. [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 call on it for "any English language AI job". [170] [171]
Text generation

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

The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his coworkers, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and procedure long-range dependencies by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative variations initially released to the public. The full version of GPT-2 was not instantly launched due to issue about potential misuse, consisting of applications for composing fake news. [174] Some specialists revealed uncertainty that GPT-2 positioned a substantial hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology 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 total version of the GPT-2 language model. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision 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 more 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 avoids certain issues encoding vocabulary with word tokens by utilizing 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 not being watched transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete version of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as few as 125 million criteria were also trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 significantly improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or encountering the essential capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released 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 personal 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 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 released in personal beta. [194] According to OpenAI, the model can working code in over a dozen programming languages, most successfully in Python. [192]
Several issues with problems, style flaws and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of discharging copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would stop 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), capable of accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar exam 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 also check out, analyze or generate as much as 25,000 words of text, and compose code in all major programming languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various 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 released GPT-4o, forum.batman.gainedge.org which can process and generate text, images and audio. [204] GPT-4o attained modern results in 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) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released 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 expects it to be especially helpful for business, wavedream.wiki start-ups and 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 created to take more time to believe about their reactions, causing greater precision. These designs are especially efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [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 reasoning design. OpenAI also unveiled o3-mini, a lighter and much faster version of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
Deep research study

Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic resemblance between text and images. It can notably be utilized for image classification. [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 handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can create pictures of realistic things ("a stained-glass window with a picture of a blue strawberry") in addition to 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 design with more realistic results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new rudimentary system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful design better able to create images from complex 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 model that can generate videos based upon short detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of created videos is unidentified.

Sora's development group named it after the Japanese word for "sky", to symbolize its "limitless innovative capacity". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos licensed for that function, however 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, specifying that it could generate videos as much as one minute long. It also shared a technical report highlighting the techniques used to train the design, and the model's capabilities. [225] It acknowledged some of its imperfections, consisting of battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but noted that they need to have been cherry-picked and wakewiki.de might not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to generate sensible video from text descriptions, mentioning its possible to transform storytelling and content production. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly plans 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 big dataset of varied audio and is also a multi-task design that can perform multilingual speech acknowledgment in addition to speech translation and language identification. [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 generate songs with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to begin fairly however then fall under chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for archmageriseswiki.com 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 generate 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 specified the tunes "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable space" in between Jukebox and human-generated music. The Verge mentioned "It's technically remarkable, even if the results seem like mushy variations of tunes that may feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The purpose is to research 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 significant layer and nerve cell of 8 neural network models which are typically studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various variations of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool constructed 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.