Announced in 2016, Gym is an open-source Python library developed to facilitate the development of support knowing algorithms. It aimed to standardize how environments are specified in AI research study, making released research more quickly reproducible [24] [144] while providing users with an easy user interface for communicating with these environments. In 2022, new advancements of Gym have actually been relocated 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 enhancing agents to solve single tasks. Gym Retro gives the ability to generalize in between video games with similar concepts however various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially do not have understanding of how to even stroll, but are offered the objectives of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the agents learn how to adapt to changing conditions. When an agent is then gotten rid of 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 learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could create an intelligence "arms race" that might increase an agent's ability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high ability level completely through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation occurred at The International 2017, the annual best champion competition for the video game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for 2 weeks of real time, and that the knowing software was a step in the direction of producing software application that can handle complicated jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement learning, as the bots find out in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy 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 were able 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 expert gamers, however ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs 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 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 demonstrated making use of deep support knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, hb9lc.org Dactyl utilizes device learning to train a Shadow Hand, a human-like robot hand, to manipulate physical objects. [167] It discovers entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation problem by utilizing domain randomization, a simulation method which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB cameras to allow the robot to control an arbitrary things by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing gradually more hard environments. ADR differs from manual domain randomization by not needing 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 models established by OpenAI" to let developers call on it for "any English language AI job". [170] [171]
Text generation

The business has popularized 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 written by Alec Radford and his coworkers, and released 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 diverse 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 design ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative versions initially released to the general public. The full variation of GPT-2 was not instantly launched due to concern about possible abuse, consisting of applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 postured a substantial risk.

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 technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language model. [177] Several sites host interactive presentations of various of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose students, shown by GPT-2 attaining advanced accuracy and perplexity on 7 of 8 zero-shot tasks (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 issues encoding vocabulary with word tokens by utilizing byte pair encoding. This allows representing any string of characters by encoding both individual 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 specified that the complete version of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as few as 125 million specifications were also trained). [186]
OpenAI stated that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the purpose 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 between English and German. [184]
GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or encountering the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched to the public for issues of possible abuse, although OpenAI prepared 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 private beta. [194] According to OpenAI, the model can develop working code in over a lots shows languages, many successfully in Python. [192]
Several concerns with problems, style flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of releasing copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would terminate 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 revealed that the updated technology passed a simulated law school bar examination 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 likewise read, evaluate or create approximately 25,000 words of text, and compose code in all significant programming languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various technical details and data about GPT-4, such as the accurate size of the model. [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 results in voice, multilingual, and vision standards, setting 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 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 anticipates it to be especially helpful for business, startups and developers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been developed to take more time to believe about their responses, leading to higher precision. These models are particularly efficient in science, coding, and reasoning tasks, 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 design. OpenAI likewise revealed o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecoms providers O2. [215]
Deep research study

Deep research study is a representative established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform extensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic resemblance in between text and images. It can significantly be utilized 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 uses a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather purse formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can create images of practical items ("a stained-glass window with an image of a blue strawberry") as well as things that do not exist in truth ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an upgraded variation of the model with more reasonable outcomes. [219] In December 2022, OpenAI released 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 announced DALL-E 3, a more effective model much better able to generate images from complex descriptions without manual prompt engineering and render complex details like hands and text. [221] It was released to the general public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can create videos based upon short detailed prompts [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora's development group called it after the Japanese word for "sky", to signify its "endless imaginative capacity". [223] Sora's technology is an adjustment of the innovation 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 purpose, but did not expose 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, mentioning that it could create videos as much as one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the design's capabilities. [225] It acknowledged a few of its imperfections, consisting of struggles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they should have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually revealed significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to create realistic video from text descriptions, mentioning its prospective to transform storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had decided to stop briefly prepare for expanding 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 also 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 songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall under turmoil the longer it plays. [230] [231] In popular culture, initial 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 generate 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 mentioned the tunes "show local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge specified "It's technologically outstanding, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider mentioned "remarkably, a few of the resulting songs are catchy and sound legitimate". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches makers to debate toy problems in front of a human judge. The function is to research study whether such a technique 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 significant layer and neuron of 8 neural network models which are typically studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various variations of Inception, and various versions 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 interface that allows users to ask questions in natural language. The system then responds with a response within seconds.