GitLab

Announced in 2016, Gym is an open-source Python library created to assist in the development of support 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 a simple user interface for communicating with these environments. In 2022, new developments of Gym have been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to fix single tasks. Gym Retro gives the ability to generalize between games with similar ideas but different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack understanding of how to even stroll, however are provided the goals of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and put in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might create an intelligence "arms race" that might increase a representative's capability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team 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 ability level entirely through experimental algorithms. Before becoming a team of 5, the first public demonstration took place at The International 2017, the yearly premiere championship tournament for the video game, where Dendi, an expert 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 actually learned by playing against itself for two weeks of genuine time, and that the knowing software application was a step in the instructions of producing software application that can manage intricate tasks 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 goals. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a complete group of 5, and they had the ability to beat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert gamers, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later on 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 systems in Dota 2's bot player reveals the obstacles of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown using deep reinforcement learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It discovers entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation issue by utilizing domain randomization, a simulation method which exposes the student to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, likewise has RGB electronic cameras to enable the robotic to control an approximate item by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl might fix a Rubik's Cube. The robot had the ability to solve 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 robustness of Dactyl to perturbations by utilizing 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 varieties. [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 job". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding 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 not being watched transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative versions at first released to the general public. The complete variation of GPT-2 was not instantly launched due to issue about prospective abuse, consisting of applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 positioned a significant threat.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake news". [175] Other researchers, 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 hush 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 presentations of various circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose students, highlighted by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not additional trained on any task-specific input-output examples).

The corpus it was trained on, bytes-the-dust.com called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids 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 without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were also trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or encountering the essential capability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the general public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month totally free personal beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed solely to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has in addition 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 design can produce working code in over a dozen programs languages, a lot of efficiently in Python. [192]
Several issues with glitches, design defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of discharging copyrighted code, with no 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 announced 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 leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, analyze or garagesale.es generate approximately 25,000 words of text, and compose 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 version, with the caution that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose different technical details and data about GPT-4, such as the exact size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art outcomes in voice, multilingual, kigalilife.co.rw 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) criteria compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o replacing 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 beneficial for enterprises, start-ups 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 actually been designed to take more time to think of their reactions, resulting in greater accuracy. These designs are particularly reliable in science, coding, 89u89.com and thinking 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, 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI likewise revealed o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, kigalilife.co.rw 2025, safety and security researchers had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to avoid confusion with telecoms providers 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 model to carry out extensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, 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 evaluate the semantic similarity 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 develops 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 handbag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can produce images of practical 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"). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an upgraded version of the model with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new simple 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 powerful design better able to create images from complex descriptions without manual timely engineering and render complex details like hands and text. [221] It was launched to the general public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can generate videos based on short detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The maximal length of created videos is unknown.

Sora's development team named it after the Japanese word for "sky", to signify its "unlimited innovative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos accredited for that purpose, however did not expose the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might generate videos up to one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the model's capabilities. [225] It acknowledged some of its imperfections, including struggles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", bytes-the-dust.com however 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 shown substantial 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 decided to stop briefly plans for expanding his Atlanta-based film 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 varied audio and is also a multi-task model that can carry out multilingual speech recognition along with speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a tune produced by tends to begin fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary 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 produce 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 mentioned the tunes "show local musical coherence [and] follow traditional chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a significant space" in between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider mentioned "surprisingly, some of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches devices to dispute toy problems in front of a human judge. The purpose is to research whether such an approach may help in auditing AI choices 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 developed to examine the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different versions of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that offers a conversational interface that permits users to ask questions in natural language. The system then responds with an answer within seconds.