GitLab

Announced in 2016, Gym is an open-source Python library created to assist in the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, making released research study more easily reproducible [24] [144] while offering users with an easy user interface for communicating with these environments. In 2022, new advancements 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 on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to fix single jobs. Gym Retro gives the capability to generalize between video games with similar principles but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have knowledge of how to even walk, but are given the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents find out how to adapt to changing conditions. When a representative is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could develop an intelligence "arms race" that might increase a representative's capability to function 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 discover to play against human players at a high ability level totally through trial-and-error algorithms. Before becoming a group of 5, the first public presentation occurred at The International 2017, the annual premiere champion tournament for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of real time, and that the learning software was a step in the direction of creating software that can manage complicated jobs like a surgeon. [152] [153] The system utilizes a kind of support knowing, as the bots find out gradually by playing against themselves hundreds of 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 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 players, but wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the video 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 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 gamer shows the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown using deep support learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker finding out to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It finds out entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation problem by using domain randomization, a simulation method which exposes the student to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB electronic cameras to allow the robot to control an arbitrary things by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might solve a Rubik's Cube. The robot had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating progressively harder environments. ADR differs from manual domain randomization by not requiring a human to specify randomization ranges. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing new AI designs developed by OpenAI" to let developers call on it for "any English language AI job". [170] [171]
Text generation

The business has actually promoted generative pretrained transformers (GPT). [172]
OpenAI's original GPT design ("GPT-1")

The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world knowledge and procedure long-range dependences by pre-training on a varied corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions initially launched to the public. The complete variation of GPT-2 was not right away released due to issue about potential abuse, including applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 positioned a significant hazard.

In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation to completely 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 released the complete version of the GPT-2 language model. [177] Several sites host interactive demonstrations 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 learners, illustrated by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (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 a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing 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 a without supervision transformer language design and the successor 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 full variation of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were likewise trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
GPT-3 significantly improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models could be approaching or coming across 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 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 OpenAI prepared 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 certified solely 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 launched in personal beta. [194] According to OpenAI, the design can produce working code in over a dozen shows languages, a lot of efficiently in Python. [192]
Several problems with problems, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been accused 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 announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar examination 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, examine or produce up to 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the version of ChatGPT using 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 revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose different technical details and data 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, multilingual, and vision benchmarks, setting brand-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 released GPT-4o mini, a smaller variation of GPT-4o changing 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 helpful for enterprises, start-ups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been designed to take more time to think of their reactions, causing greater accuracy. These models 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 unveiled o3, the follower of the o1 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are evaluating 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 instead of o2 to avoid confusion with telecommunications companies 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 model to carry out extensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and forum.batman.gainedge.org Python tools allowed, it reached an accuracy 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 evaluate the semantic resemblance 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 uses a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can create images of realistic objects ("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"). As of March 2021, engel-und-waisen.de no API or code is available.

DALL-E 2

In April 2022, wiki.snooze-hotelsoftware.de OpenAI revealed DALL-E 2, an updated version of the model with more reasonable results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-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 design much better able to create images from complicated descriptions without manual timely engineering and render complicated 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 backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.

Sora's advancement group named it after the Japanese word for "sky", to signify its "endless imaginative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited 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, stating that it could produce videos approximately one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the design's abilities. [225] It acknowledged some of its drawbacks, consisting of struggles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", however noted that they need to have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, significant entertainment-industry figures have actually revealed substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to generate practical video from text descriptions, citing its prospective to reinvent storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause prepare for broadening his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, yewiki.org Whisper is a general-purpose speech recognition design. [228] It is trained on a large dataset of varied audio and is likewise a multi-task design that can perform 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 produce tunes with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to start fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, 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 create music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs tune samples. OpenAI mentioned the songs "show local musical coherence [and] follow standard chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" which "there is a considerable gap" between Jukebox and human-generated music. The Verge mentioned "It's highly remarkable, even if the outcomes seem like mushy variations of songs that might feel familiar", while Business Insider specified "surprisingly, some of the resulting songs are memorable and sound legitimate". [234] [235] [236]
Interface

Debate Game

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

Released in 2020, wiki.myamens.com Microscope [239] is a collection of visualizations of every significant layer and neuron of eight neural network designs which are frequently studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

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