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 defined in AI research study, making published research study more quickly reproducible [24] [144] while supplying users with a simple interface for engaging with these environments. In 2022, brand-new developments 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 on video games [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing agents to fix single jobs. Gym Retro offers the capability to generalize between games with similar concepts however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even walk, but are offered the goals of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the agents find out how to adapt to altering conditions. When a representative is then removed from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives could create an intelligence "arms race" that could increase a representative'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 computer game Dota 2, that discover to play against human players at a high skill level totally through experimental algorithms. Before becoming a team of 5, the first occurred at The International 2017, the annual best championship competition for the video game, where Dendi, a professional 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 learned by playing against itself for two weeks of actual time, which the learning software application was a step in the direction of creating software application that can manage intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of support knowing, as the bots find out in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a full group of 5, and they were able to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 total 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 reveals the challenges of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually shown making use of deep reinforcement knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It discovers entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by using domain randomization, a simulation method which exposes the student to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB video cameras to allow the robotic to manipulate an approximate 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 could resolve a Rubik's Cube. The robotic was able to resolve 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 toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of creating gradually 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 models developed by OpenAI" to let developers call on it for "any English language AI task". [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 written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world knowledge and process long-range reliances 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 initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only 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 specialists revealed uncertainty that GPT-2 posed a significant risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other scientists, such as Jeremy Howard, warned 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 difficult to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language design. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose students, highlighted by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain problems 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 without supervision transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as few as 125 million criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and could generalize the function 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 in between English and German. [184]
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or experiencing the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 needed several 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 released to the general public for issues of possible abuse, although OpenAI prepared to allow 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 certified solely 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 launched in private beta. [194] According to OpenAI, the model can create working code in over a dozen programs languages, most efficiently in Python. [192]
Several concerns with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of giving off copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would discontinue 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 announced that the upgraded innovation 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 might also check out, evaluate or generate as much as 25,000 words of text, and write code in all major programs languages. [200]
Observers reported that the iteration 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 issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal numerous technical details and data about GPT-4, such as the exact size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained modern outcomes 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) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized variation 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, start-ups and developers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been designed to take more time to believe about their responses, causing greater precision. These models 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 changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning model. OpenAI likewise revealed o3-mini, a lighter and quicker version of OpenAI o3. Since December 21, 2024, this design 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 scientists had the chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent 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 design to perform extensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image classification

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 significantly be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and create corresponding images. It can develop 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"). As of March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an upgraded version of the design with more realistic results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new simple system for transforming 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 create images from complicated descriptions without manual prompt engineering and render complex details like hands and text. [221] It was launched to the 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 short detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution up to 1920x1080 or engel-und-waisen.de 1080x1920. The optimum length of generated videos is unidentified.

Sora's advancement team named it after the Japanese word for "sky", to symbolize its "limitless innovative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for that function, 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, specifying that it might produce 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 some of its drawbacks, consisting of battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but noted that they should have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have shown considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to create sensible video from text descriptions, citing its potential to reinvent storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to stop briefly plans for broadening his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition design. [228] It is trained on a big dataset of diverse audio and is also a multi-task design that can carry out 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 generate songs with 10 instruments in 15 styles. According to The Verge, a tune produced 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 utilized as early as 2020 for the web mental thriller Ben Drowned to create 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 bit of lyrics and outputs song samples. OpenAI stated the songs "show local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" which "there is a significant gap" between Jukebox and human-generated music. The Verge mentioned "It's highly outstanding, even if the outcomes sound like mushy variations of songs that may feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are appealing and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches makers to debate toy issues 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 produced to evaluate the functions that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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