Announced in 2016, Gym is an open-source Python library created to help with the advancement of support learning algorithms. It aimed to standardize how environments are specified in AI research, making released research study more easily reproducible [24] [144] while offering users with an easy user interface for interacting with these environments. In 2022, new developments of Gym have been moved to the library Gymnasium. [145] [146]
Gym Retro

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

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack understanding of how to even walk, however are given the goals of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents find out how to adapt to altering conditions. When an agent is then removed from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents could create an intelligence "arms race" that could increase a representative's capability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that find out to play against human gamers at a high ability level entirely through experimental algorithms. Before becoming a team of 5, the very first public presentation took place at The International 2017, the annual premiere championship competition for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of actual time, and that the learning software application was a step in the direction of developing software that can handle complex jobs like a surgeon. [152] [153] The system utilizes a kind of support knowing, 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 enemy and taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full team of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional players, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the video game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look 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 systems in Dota 2's bot player shows the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has actually demonstrated making use of deep support knowing (DRL) representatives 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 robotic hand, to control physical objects. [167] It finds out entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB electronic cameras to enable the robotic to control an arbitrary object 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 demonstrated that Dactyl could 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 design. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of generating progressively more tough environments. ADR differs from manual domain randomization by not requiring 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 designs developed by OpenAI" to let developers contact it for "any English language AI job". [170] [171]
Text generation

The company 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 model was written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative model of language might obtain world knowledge and process long-range reliances by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just minimal demonstrative variations at first released to the general public. The full variation of GPT-2 was not right away launched due to concern about possible misuse, including applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 positioned a substantial danger.

In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted of "the innovation to absolutely 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 total version of the GPT-2 language model. [177] Several sites host interactive presentations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose students, setiathome.berkeley.edu shown by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not additional 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 at least 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by using 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 without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI stated 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 designs with as couple of as 125 million parameters were also trained). [186]
OpenAI mentioned that GPT-3 was successful 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 between English and Romanian, and between English and German. [184]
GPT-3 significantly improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or coming across the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately released to the public for issues of possible abuse, although OpenAI planned to allow 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 exclusively 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 personal beta. [194] According to OpenAI, the design can produce working code in over a dozen programming languages, the majority of effectively in Python. [192]
Several problems with problems, design defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of emitting copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would cease 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 innovation passed a simulated law school bar examination with a rating 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, analyze or generate approximately 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and statistics about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision criteria, 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 released GPT-4o mini, a smaller sized 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 expects it to be particularly useful for enterprises, startups and designers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been created to take more time to consider their responses, causing greater precision. These models are especially effective in science, coding, and reasoning 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 thinking design. OpenAI likewise revealed o3-mini, a lighter and much faster version of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications companies O2. [215]
Deep research study

Deep research study 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, information 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 especially 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 purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can create images of sensible ("a stained-glass window with a picture of a blue strawberry") along with 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 revealed DALL-E 2, an upgraded version of the model with more reasonable results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a 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 powerful design much better able to generate images from complex descriptions without manual timely engineering and render complex details like hands and text. [221] It was released to the public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can create videos based upon short detailed prompts [223] along with extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's development team called it after the Japanese word for "sky", to symbolize its "endless innovative potential". [223] Sora's technology is an adjustment 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 accredited for that purpose, but did not expose the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it might create videos up to 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 shortcomings, including struggles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they should have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually revealed considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's capability to produce sensible video from text descriptions, citing its prospective to revolutionize storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had decided to pause plans for expanding his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of varied audio and is also a multi-task design that can carry out multilingual speech recognition along with speech translation and language identification. [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 create tunes with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly but then fall under chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web psychological 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 tune samples. OpenAI stated the tunes "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a considerable space" in between Jukebox and human-generated music. The Verge stated "It's technically impressive, even if the results sound like mushy variations of tunes that may feel familiar", while Business Insider mentioned "surprisingly, some of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to debate toy problems in front of a human judge. The purpose is to research whether such a technique 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 substantial layer and nerve cell of 8 neural network designs which are often studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks easily. The models 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 offers a conversational user interface that allows users to ask concerns in natural language. The system then responds with an answer within seconds.