GitLab

Announced in 2016, Gym is an open-source Python created to assist in the advancement of support knowing algorithms. It aimed to standardize how environments are specified in AI research, making published research more easily reproducible [24] [144] while offering users with an easy user interface for engaging with these environments. In 2022, new developments 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 on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing representatives to fix single jobs. Gym Retro offers the capability to generalize between games with similar concepts but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have understanding of how to even walk, however are given the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents find out how to adjust to altering conditions. When a representative is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had actually found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might create an intelligence "arms race" that might increase an agent's capability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high ability level totally through experimental algorithms. Before becoming a group of 5, the very first public presentation took place at The International 2017, the annual best champion competition for gratisafhalen.be 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 two weeks of actual time, and that the learning software application was an action in the instructions of producing software that can manage complicated tasks like a surgeon. [152] [153] The system uses a kind of reinforcement learning, 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 objectives. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a full team of 5, and they were able to beat groups 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 reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public appearance came later that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot player reveals the obstacles of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated using deep reinforcement learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker discovering to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It discovers totally in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, likewise has RGB cams to enable the robotic to control an arbitrary things by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating progressively more tough environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

In June 2020, OpenAI announced 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 job". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language could obtain world understanding and process long-range dependencies by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative versions initially launched to the general public. The full variation of GPT-2 was not instantly launched due to concern about potential misuse, consisting of applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 positioned a significant threat.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake 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 difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched 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 additional 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 prevents certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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 design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion parameters, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186]
OpenAI stated 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 provided examples of translation and cross-linguistic transfer learning between English and Romanian, and higgledy-piggledy.xyz between English and German. [184]
GPT-3 drastically enhanced 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 needed several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the public for issues of possible abuse, wiki.lafabriquedelalogistique.fr although OpenAI planned to enable gain access to through a paid cloud API after a two-month free private beta that began 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 private beta. [194] According to OpenAI, the model can develop working code in over a lots programs languages, most efficiently in Python. [192]
Several concerns with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of discharging copyrighted code, with no author attribution or license. [197]
OpenAI revealed 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 updated 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, examine or generate approximately 25,000 words of text, and compose code in all significant programs languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal numerous technical details and statistics about GPT-4, such as the accurate 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 cutting edge lead to voice, kigalilife.co.rw multilingual, and vision standards, setting brand-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 launched GPT-4o mini, a smaller sized 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 particularly beneficial for business, startups and developers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been created to take more time to think of their reactions, resulting in greater precision. These designs are particularly efficient in science, coding, and reasoning jobs, 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, wiki.eqoarevival.com 2024, OpenAI unveiled o3, the successor of the o1 reasoning design. OpenAI likewise unveiled o3-mini, a lighter and faster version of OpenAI o3. Since December 21, bytes-the-dust.com 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 models. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications providers O2. [215]
Deep research

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out comprehensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching 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 model that is trained to examine 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 model 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 bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can develop images of sensible objects ("a stained-glass window with an image of a blue strawberry") along with 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 announced DALL-E 2, an updated version of the design with more sensible results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new rudimentary system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more effective design better able to generate images from complicated descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was released 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 short detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of created videos is unidentified.

Sora's development group called it after the Japanese word for "sky", to represent its "endless imaginative potential". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos certified for that function, however did not reveal the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could generate videos approximately one minute long. It likewise shared a technical report highlighting the techniques used to train the design, and the design's capabilities. [225] It acknowledged some of its shortcomings, including battles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", 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 awe at the innovation's capability to produce sensible video from text descriptions, citing its prospective to change storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause prepare for broadening his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of varied audio and is also a multi-task design that can carry out 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 predict subsequent musical notes in MIDI music files. It can generate songs with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start fairly but then fall under turmoil 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, oeclub.org artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the tunes "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" which "there is a significant gap" in between Jukebox and human-generated music. The Verge mentioned "It's technologically excellent, even if the results seem like mushy variations of songs that might feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are appealing and sound legitimate". [234] [235] [236]
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 an approach may assist in auditing AI choices 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 designs which are often studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, different versions of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

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