Announced in 2016, Gym is an open-source Python library designed to assist in the development of support knowing algorithms. It aimed to standardize how environments are specified in AI research, making published research more easily reproducible [24] [144] while offering users with an easy user interface for connecting with these environments. In 2022, new advancements of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support knowing (RL) research on video games [147] utilizing RL algorithms and research study generalization. Prior RL research focused mainly on optimizing representatives to solve single tasks. Gym Retro gives the capability to generalize in between games with similar concepts but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first lack understanding of how to even stroll, but are given the goals of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents learn how to adjust to altering conditions. When an agent is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives could develop 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 team of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that find out to play against human players at a high ability level totally through trial-and-error algorithms. Before becoming a team of 5, the very first public demonstration happened at The International 2017, the annual best champion competition for the video game, where Dendi, an expert Ukrainian player, yewiki.org lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for ratemywifey.com two weeks of genuine time, and that the learning software was an action in the direction of creating software application that can deal with intricate jobs like a cosmetic surgeon. [152] [153] The system utilizes a kind of support 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 enemy and taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full group 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 exhibit matches against expert gamers, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs 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 that month, hb9lc.org where they played in 42,729 overall 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 player reveals the obstacles of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated the use of deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

in 2018, Dactyl uses machine learning to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It finds out entirely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB electronic cameras to permit the robotic to manipulate an approximate object 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 solve a Rubik's Cube. The robotic was able 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 robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually harder environments. ADR varies from manual domain randomization by not needing a human to define randomization varieties. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-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 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 design of language might obtain world understanding and process long-range dependencies by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

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

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other scientists, such as Jeremy Howard, warned 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 difficult to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language design. [177] Several websites host interactive presentations 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 learners, highlighted by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (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 at least 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 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 larger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as few as 125 million criteria were also trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" jobs and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing in between English and Romanian, and between English and German. [184]
GPT-3 dramatically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or experiencing the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of 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 model was not instantly launched to the public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month complimentary 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 released in private beta. [194] According to OpenAI, the design can produce working code in over a lots programs languages, the majority of successfully in Python. [192]
Several problems with glitches, design defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of discharging copyrighted code, without any author attribution or license. [197]
OpenAI revealed that they would cease 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), efficient in accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar test with a rating 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 up to 25,000 words of text, and write code in all major shows languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has decreased to expose numerous technical details and stats about GPT-4, such as the exact size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision benchmarks, 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 sized version of GPT-4o changing 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 anticipates it to be particularly beneficial for business, start-ups and developers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been developed to take more time to think about their responses, causing higher accuracy. These models are especially effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Team members. [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 model. OpenAI also unveiled o3-mini, a lighter and much faster version of OpenAI o3. As of 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, 2025, security and security scientists had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications services service provider 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 perform substantial web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 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 model that is trained to examine the semantic resemblance between text and images. It can significantly be used for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to analyze natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can create pictures of reasonable things ("a stained-glass window with an image of a blue strawberry") in addition to 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 updated version of the design with more sensible results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new simple system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful design 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 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 up to 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.

Sora's advancement group named it after the Japanese word for "sky", to represent its "unlimited innovative capacity". [223] Sora's innovation 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 certified for that purpose, but did not reveal the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could create videos up to one minute long. It also shared a technical report highlighting the methods utilized to train the model, and the design's abilities. [225] It acknowledged a few of its shortcomings, including battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they need to have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to create practical video from text descriptions, citing its prospective to change storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to pause plans for expanding his Atlanta-based motion picture 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 likewise a multi-task model that can carry out multilingual speech acknowledgment as well as 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 generate tunes with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized 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 create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs tune samples. OpenAI mentioned the tunes "show local musical coherence [and] follow standard chord patterns" however acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a significant space" between Jukebox and human-generated music. The Verge stated "It's highly remarkable, even if the outcomes seem like mushy variations of songs that may feel familiar", while Business Insider specified "remarkably, 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 makers to discuss toy problems in front of a human judge. The purpose is to research study whether such a technique might help in auditing AI decisions and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of 8 neural network models which are frequently studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different variations of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

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