Announced in 2016, Gym is an open-source Python library designed to assist in the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research study, making released research more quickly reproducible [24] [144] while supplying users with a basic user interface for engaging with these environments. In 2022, brand-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 on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to resolve single tasks. Gym Retro offers the capability to generalize in between games with comparable ideas however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first lack knowledge of how to even walk, however are offered the goals of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents discover how to adapt to changing 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, recommending it had found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might produce an intelligence "arms race" that could increase a representative's ability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human players at a high ability level entirely through experimental algorithms. Before becoming a group of 5, the very first public presentation occurred at The International 2017, the yearly premiere championship tournament for the video game, where Dendi, an expert 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 discovered by playing against itself for 2 weeks of genuine time, and that the knowing software was an action in the direction of creating software application that can manage complicated jobs like a surgeon. [152] [153] The system uses a kind of support knowing, as the bots discover in time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a complete team 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 expert players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot gamer reveals the challenges of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown using deep support learning (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 robotic hand, to control physical things. [167] It discovers entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, also has RGB cameras to permit the robotic to control an approximate things by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could solve a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating progressively harder environments. ADR varies from manual domain randomization by not needing a human to define randomization varieties. [169]
API

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

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

The original paper on generative pre-training of a transformer-based language model was written 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 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 initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only limited demonstrative variations at first launched to the public. The complete variation of GPT-2 was not instantly launched due to concern about potential abuse, including applications for composing phony news. [174] Some professionals expressed uncertainty that GPT-2 presented a considerable danger.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural phony news". [175] Other scientists, such as Jeremy Howard, warned 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 difficult to filter". [176] In November 2019, OpenAI launched the complete 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 unsupervised language designs to be general-purpose learners, shown by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not further trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by using byte pair encoding. This allows representing any string of characters by encoding both private 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 model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as few as 125 million specifications were also trained). [186]
OpenAI specified that GPT-3 was successful 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 in between English and Romanian, and 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 coming across the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the general public for concerns of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month complimentary private beta that started 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 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 released in personal beta. [194] According to OpenAI, the model can develop working code in over a lots shows languages, many successfully in Python. [192]
Several problems with problems, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of emitting copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would terminate support 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 image inputs. [199] They revealed that the upgraded technology passed a simulated law school bar exam 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 might likewise read, analyze or generate as much as 25,000 words of text, and compose code in all significant programming languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal numerous technical details and statistics 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 cutting edge lead to voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition 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 version 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 designers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been created to take more time to believe about their actions, resulting in greater precision. These models are especially effective 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 reasoning model. OpenAI also revealed o3-mini, a lighter and quicker variation of OpenAI o3. As of 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 chance to obtain early access to these models. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications services provider O2. [215]
Deep research study

Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform extensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached a precision 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 examine the semantic resemblance in between text and images. It can especially be used for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that produces 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 generate matching images. It can produce pictures of realistic items ("a stained-glass window with a picture of a blue strawberry") as well as objects 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 announced DALL-E 2, an updated version of the model with more practical outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new primary 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 design much better able to generate images from complex descriptions without manual 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 produce videos based on brief detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora's development team called it after the Japanese word for "sky", to represent its "unlimited innovative potential". [223] Sora's innovation is an adaptation 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 accredited for that function, however did not expose the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could generate videos approximately one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the design's abilities. [225] It acknowledged a few of its drawbacks, consisting of struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they should have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have revealed significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to produce reasonable video from text descriptions, mentioning its prospective to reinvent storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause plans for broadening his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of diverse audio and is also a multi-task design that can perform multilingual speech acknowledgment as well as speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, a tune created by MuseNet tends to start fairly however then fall into turmoil the longer it plays. [230] [231] In pop culture, forum.batman.gainedge.org preliminary applications of this tool were utilized as early as 2020 for the internet 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 bit of lyrics and outputs tune samples. OpenAI stated the tunes "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a significant gap" in between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes seem like mushy versions of tunes that might feel familiar", while Business Insider mentioned "surprisingly, some of the resulting songs are memorable and sound genuine". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to debate toy issues in front of a human judge. The function is to research study whether such an approach may help in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of eight neural network models which are often studied in interpretability. [240] Microscope was created to analyze the features that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various versions of Inception, and various versions 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 responds with an answer within seconds.