GitLab

Announced in 2016, Gym is an open-source Python library developed to help with the advancement of support learning algorithms. It aimed to standardize how environments are specified in AI research study, making released research more quickly reproducible [24] [144] while offering users with an easy user interface for interacting with these environments. In 2022, brand-new developments of Gym have 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 study focused mainly on enhancing agents to fix single tasks. Gym Retro offers the ability to generalize in between video games with similar ideas however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack knowledge of how to even walk, however are provided the objectives of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives discover how to adjust to altering conditions. When an agent is then eliminated from this virtual environment and placed in a brand-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 could produce an intelligence "arms race" that might increase an agent's capability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high skill level totally through trial-and-error algorithms. Before ending up being a team of 5, the first public presentation took place at The International 2017, the yearly premiere championship competition for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of actual time, which the learning software was an action in the instructions of producing software that can handle complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement knowing, as the bots find out with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full team of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional gamers, 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 exhibition match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's systems in Dota 2's bot player shows the obstacles of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually shown making use of deep support learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine finding out to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It discovers entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation problem by utilizing domain randomization, a simulation method which exposes the student to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has RGB cameras to enable the robot to manipulate an approximate things by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and it-viking.ch an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot had the ability 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 enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of producing gradually harder environments. ADR varies from manual domain randomization by not requiring a human to specify randomization ranges. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new AI models developed by OpenAI" to let developers get in touch with 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 design ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative model of language might obtain world knowledge and process long-range dependences 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 design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative versions initially launched to the general public. The complete version of GPT-2 was not instantly released due to issue about prospective abuse, including applications for composing phony news. [174] Some specialists expressed uncertainty that GPT-2 postured a significant hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to totally 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 variation of the GPT-2 language model. [177] Several websites host interactive demonstrations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose students, highlighted by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (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 a minimum of 3 upvotes. It prevents certain problems encoding vocabulary with word tokens by using byte pair encoding. This permits 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 a not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were likewise trained). [186]
OpenAI stated that GPT-3 prospered at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing 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 experiencing the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the general public for concerns of possible abuse, although to permit gain access to through a paid cloud API after a two-month complimentary personal 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 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 design can produce working code in over a lots programs languages, most successfully in Python. [192]
Several issues with problems, style defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of producing copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would cease 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 test 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, examine or create approximately 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and statistics about GPT-4, such as the precise size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting edge outcomes in voice, multilingual, and vision standards, setting 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 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 wiki.lafabriquedelalogistique.fr GPT-4o. OpenAI anticipates it to be especially useful for enterprises, startups 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 been designed to take more time to believe about their reactions, causing greater accuracy. These designs are particularly effective in science, coding, and wiki.snooze-hotelsoftware.de thinking jobs, and archmageriseswiki.com were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and faster version of OpenAI o3. Since 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 instead of o2 to avoid confusion with telecommunications providers O2. [215]
Deep research study

Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out substantial web browsing, data analysis, 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 design that is trained to analyze the semantic similarity in between text and images. It can notably be used 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 utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can create pictures of realistic things ("a stained-glass window with an image of a blue strawberry") as well as things 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 revealed DALL-E 2, an upgraded version of the design with more reasonable results. [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 model. [220]
DALL-E 3

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

Sora

Sora is a text-to-video model that can generate videos based upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.

Sora's development team called it after the Japanese word for "sky", to signify its "limitless creative potential". [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 in addition to copyrighted videos licensed for that purpose, but did not reveal the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might create videos as much as one minute long. It also shared a technical report highlighting the techniques utilized to train the model, and the model's capabilities. [225] It acknowledged some of its imperfections, consisting of battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they should have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have revealed considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to create sensible video from text descriptions, mentioning its prospective to revolutionize storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause plans for expanding his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [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 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 tunes with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to start fairly but then fall under chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to produce music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song samples. OpenAI specified the tunes "show local musical coherence [and] follow conventional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge stated "It's technically outstanding, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting tunes are memorable and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to debate toy issues in front of a human judge. The purpose is to research whether such an approach might 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 substantial layer and nerve cell of eight neural network models which are frequently studied in interpretability. [240] Microscope was created to examine the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different variations of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that provides a conversational user interface that allows users to ask questions in natural language. The system then reacts with a response within seconds.