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 quickly reproducible [24] [144] while supplying users with an easy user interface for connecting with these environments. In 2022, new developments of Gym have actually been transferred to the library Gymnasium. [145] [146]

Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to solve single jobs. Gym Retro offers the ability to generalize between games with similar principles however various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack knowledge of how to even stroll, but are offered the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and placed in a brand-new virtual environment with high winds, the representative 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 in between representatives might produce an intelligence "arms race" that could increase an agent's capability to operate even outside the context of the competitors. [148]

OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human players at a high skill level totally through trial-and-error algorithms. Before becoming a group of 5, the first public presentation happened at The International 2017, the annual best championship tournament for the 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 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 deal with complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots discover over time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156]

By June 2018, the ability of the bots expanded to play together as a complete team of 5, and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional gamers, but ended up losing both video 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' 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 gamer reveals the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has demonstrated the use of deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]

Dactyl

Developed in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It finds out entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by utilizing domain randomization, a simulation method which exposes the learner to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB electronic cameras to permit the robotic to manipulate an arbitrary object by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and an octagonal prism. [168]

In 2019, OpenAI showed that Dactyl could solve a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present 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 generating progressively harder 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 new AI designs established by OpenAI" to let developers contact it for "any English language AI job". [170] [171]

Text generation

The business has popularized generative pretrained transformers (GPT). [172]

OpenAI's initial GPT design ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language could 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 a not being watched transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative variations at first launched to the public. The full version of GPT-2 was not immediately launched due to issue about prospective misuse, including applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 posed a substantial danger.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to absolutely 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 version of the GPT-2 language design. [177] Several websites host interactive demonstrations of various 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 design 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 concerns encoding vocabulary with word tokens by utilizing 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 mentioned that the complete version of GPT-3 contained 175 billion specifications, [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 specifications were also trained). [186]

OpenAI stated 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 gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and in between English and German. [184]

GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly launched to the general public for concerns 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 began 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 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 launched in personal beta. [194] According to OpenAI, the model can develop working code in over a lots programming languages, most efficiently in Python. [192]

Several issues with glitches, style defects and security vulnerabilities were mentioned. [195] [196]

GitHub Copilot has been implicated of giving off copyrighted code, without any author attribution or license. [197]

OpenAI revealed that they would terminate 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), capable of accepting text or image inputs. [199] They announced that the updated 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 might likewise read, evaluate or generate up to 25,000 words of text, and write code in all major programming 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 caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and stats about GPT-4, such as the exact size of the design. [203]

GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, 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) criteria compared to 86.5% by GPT-4. [207]

On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized 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 business, startups and developers looking for 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 created to take more time to consider their reactions, causing higher accuracy. These designs are especially reliable in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Staff member. [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 design. OpenAI also revealed o3-mini, a lighter and faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecommunications companies O2. [215]

Deep research

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]

Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic similarity between text and images. It can notably be used for image classification. [217]

Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can create pictures of practical things ("a stained-glass window with a picture of a blue strawberry") in addition to things that do not exist in truth ("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 variation of the design with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new basic system for transforming a text description into a 3-dimensional model. [220]

DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful model 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 model that can produce videos based on brief detailed prompts [223] along with extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.

Sora's development group named it after the Japanese word for "sky", to symbolize its "limitless innovative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos licensed for that function, but 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 also shared a technical report highlighting the methods utilized to train the model, and the model's capabilities. [225] It acknowledged a few of its shortcomings, including struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but kept in mind that they need to have been cherry-picked and may not represent Sora's normal output. [225]

Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have revealed substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to create realistic video from text descriptions, 35.237.164.2 mentioning its possible to revolutionize storytelling and content production. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to pause plans for broadening his Atlanta-based movie 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 diverse audio and is also a multi-task model that can carry out multilingual speech acknowledgment in addition to speech translation and language identification. [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 produce tunes with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to start fairly but then fall into chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to create music for the titular character. [232] [233]

Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to generate music with vocals. After training on 1.2 million samples, the system accepts a genre, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the songs "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a significant space" in between Jukebox and human-generated music. The Verge specified "It's technologically excellent, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider stated "remarkably, some of the resulting tunes are memorable and sound legitimate". [234] [235] [236]

User interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches makers 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 considerable layer and nerve cell of 8 neural network models which are typically studied in interpretability. [240] Microscope was created to evaluate the functions that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various variations of Inception, and various versions 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 interface that allows users to ask questions in natural language. The system then reacts with an answer within seconds.