Announced in 2016, Gym is an open-source Python library created to facilitate the development of support learning algorithms. It aimed to standardize how environments are defined in AI research, making released research more quickly reproducible [24] [144] while providing users with an easy interface for interacting with these environments. In 2022, brand-new advancements of Gym have been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support learning (RL) research on computer game [147] using RL algorithms and research study generalization. Prior RL research focused mainly on optimizing representatives to resolve single tasks. Gym Retro gives the ability to generalize in between video games with similar principles but different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have understanding of how to even stroll, however are provided the objectives of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing process, the agents learn how to adapt to changing conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could produce an intelligence "arms race" that could increase a representative's ability to function even outside the context of the competition. [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 skill level totally through experimental algorithms. Before ending up being a team of 5, the very first public demonstration took place at The International 2017, the annual premiere champion tournament for surgiteams.com the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for 2 weeks of actual time, and that the knowing software was an action in the instructions of producing software application that can manage complex jobs like a surgeon. [152] [153] The system uses a kind of support learning, as the bots discover gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy 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 defeat 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 games. [160] [161] [162] In April 2019, OpenAI Five beat 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 look came later on that month, where they played in 42,729 total video 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 reveals the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually shown making use of deep reinforcement knowing (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 manipulate physical things. [167] It finds out entirely in simulation using 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 variety of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB video cameras to permit the robot to control an approximate object 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 showed 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 present complicated physics that is harder to design. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing progressively more challenging environments. ADR differs from manual domain randomization by not requiring a human to define randomization ranges. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new AI designs developed by OpenAI" to let designers contact 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 composed by Alec Radford and his colleagues, 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 adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative variations at first released to the general public. The complete variation of GPT-2 was not immediately released due to concern about potential misuse, including applications for composing fake news. [174] Some professionals expressed uncertainty that GPT-2 postured a considerable threat.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to totally 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 version of the GPT-2 language design. [177] Several websites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue not being watched language models to be general-purpose students, illustrated by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more 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 avoids certain concerns 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 model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were likewise trained). [186]
OpenAI specified 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 learning between English and Romanian, and between English and German. [184]
GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or coming across the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the general public for concerns of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month free personal beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually 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 personal beta. [194] According to OpenAI, the model can create working code in over a dozen shows languages, most effectively in Python. [192]
Several issues with glitches, style defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of emitting copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would discontinue assistance 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), capable of accepting text or image inputs. [199] They announced that the upgraded technology 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 could likewise check out, examine or generate as much as 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement 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 also capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different 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 released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced lead to voice, ratemywifey.com multilingual, and vision standards, 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 released GPT-4o mini, a smaller sized version 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 expects it to be especially beneficial for enterprises, startups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and trademarketclassifieds.com o1-mini models, which have actually been created to take more time to think of their responses, causing higher accuracy. These designs are especially efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [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 model. OpenAI also unveiled o3-mini, a lighter and much 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, systemcheck-wiki.de 2025, security and security scientists had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications services company O2. [215]
Deep research study

Deep research is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out extensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, 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 in between text and images. It can notably be utilized for image category. [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 translate natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can create pictures of practical things ("a stained-glass window with an image 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 revealed DALL-E 2, an upgraded version of the design with more practical results. [219] In December 2022, OpenAI released 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 effective model better able to generate images from complex descriptions without manual prompt engineering and render complicated details like hands and wiki.rolandradio.net 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 model that can produce videos based on short detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.

Sora's development group named it after the Japanese word for "sky", to signify its "unlimited creative 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 utilizing publicly-available videos as well as copyrighted videos licensed for that purpose, but did not reveal the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it might create 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 imperfections, consisting of struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but noted that they should have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown substantial interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to produce reasonable video from text descriptions, photorum.eclat-mauve.fr mentioning its possible to change storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly prepare for broadening his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of varied audio and is likewise 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 anticipate subsequent musical notes in MIDI music files. It can produce songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly but then fall into turmoil the longer it plays. [230] [231] In pop 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 produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the songs "reveal local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable space" between Jukebox and human-generated music. The Verge mentioned "It's technically impressive, even if the results sound like mushy versions of songs that might feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, hb9lc.org which teaches devices to discuss toy problems in front of a human judge. The purpose is to research whether such a method might 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 neuron of eight neural network models which are often studied in interpretability. [240] Microscope was developed to evaluate the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various variations of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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