Announced in 2016, Gym is an library developed to help with the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, making published research study more quickly reproducible [24] [144] while providing users with an easy interface for connecting with these environments. In 2022, brand-new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research on computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing representatives to resolve single jobs. Gym Retro gives the capability to generalize in between video games with comparable concepts but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first lack understanding of how to even stroll, but are given the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the agents find out how to adapt to changing conditions. When an agent is then eliminated 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 way. [148] [149] OpenAI’s Igor Mordatch argued that competitors between agents might develop an intelligence “arms race” that could increase a representative’s capability to work 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 video game Dota 2, that discover to play against human players at a high ability level totally through trial-and-error algorithms. Before ending up being a group of 5, the first public demonstration occurred at The International 2017, the yearly best champion competition for the video 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 two weeks of actual time, and that the learning software application was a step in the instructions of developing software that can manage complicated jobs like a cosmetic surgeon. [152] [153] The system utilizes a form of support knowing, as the bots discover gradually by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a full group of 5, and they had the ability to beat groups of amateur and wiki.dulovic.tech semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots’ last public look came later on that month, where they played in 42,729 total games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5’s systems in Dota 2’s bot gamer reveals the obstacles of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown the use of deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robotic hand, to control physical objects. [167] It finds out completely in simulation utilizing the exact same RL algorithms and trademarketclassifieds.com training code as OpenAI Five. OpenAI took on the object orientation issue by utilizing domain randomization, a simulation method which exposes the student to a variety of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB video cameras to allow the robot to control an arbitrary object by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik’s Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik’s Cube present intricate physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating progressively more hard environments. ADR differs from manual domain randomization by not requiring a human to define randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was “for accessing brand-new AI models developed by OpenAI” to let designers call on it for “any English language AI job”. [170] [171]
Text generation

The business has actually promoted generative pretrained transformers (GPT). [172]
OpenAI’s original GPT model (“GPT-1”)

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his coworkers, and published in preprint on OpenAI’s site on June 11, 2018. [173] It revealed how a generative model of language could obtain world knowledge and process long-range dependences 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 successor to OpenAI’s original GPT design (“GPT-1”). GPT-2 was announced in February 2019, with only limited demonstrative versions initially released to the public. The complete version of GPT-2 was not instantly launched due to concern about potential misuse, including applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 posed a significant risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot “neural phony news”. [175] Other scientists, 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 models. [178] [179] [180]
GPT-2’s authors argue without supervision language designs to be general-purpose learners, illustrated by GPT-2 attaining cutting edge precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more 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 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 an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as few as 125 million criteria were likewise trained). [186]
OpenAI stated that GPT-3 prospered at certain “meta-learning” jobs and could 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 in between English and German. [184]
GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI cautioned 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 needed numerous thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly launched to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month complimentary personal beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified solely to Microsoft. [190] [191]
Codex

Announced in mid-2021, surgiteams.com Codex is a descendant of GPT-3 that has furthermore 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 personal beta. [194] According to OpenAI, the design can create working code in over a dozen programs languages, most efficiently in Python. [192]
Several concerns with problems, design flaws and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of discharging 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 revealed the release of Generative Pre-trained Transformer 4 (GPT-4), efficient in accepting text or image inputs. [199] They revealed that the updated innovation 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 check out, examine or create approximately 25,000 words of text, and write code in all major programs languages. [200]
Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to expose various technical details and stats about GPT-4, such as the precise size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision standards, setting 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 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 expects it to be particularly helpful for enterprises, startups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been created to take more time to consider their reactions, leading to higher precision. These designs 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 revealed o3, the follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and faster variation of OpenAI o3. Since 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 designs. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications companies O2. [215]
Deep research study

Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI’s o3 model to carry out comprehensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity’s Last Exam) benchmark. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic resemblance 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 produces 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 handbag shaped like a pentagon” or “an isometric view of a sad capybara”) and create matching images. It can produce pictures of reasonable items (“a stained-glass window with an image of a blue strawberry”) as well as items that do not exist in reality (“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 updated variation of the design with more sensible results. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new primary system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more effective model better able to generate images from complex descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can generate videos based on brief detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.

Sora’s development team named it after the Japanese word for “sky”, to represent its “limitless imaginative capacity”. [223] Sora’s innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos certified for that function, 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 generate videos approximately one minute long. It likewise shared a technical report highlighting the approaches utilized to train the model, and the design’s capabilities. [225] It acknowledged some of its imperfections, consisting of struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos “impressive”, however kept in mind that they must have been cherry-picked and may not represent Sora’s normal output. [225]
Despite uncertainty from some academic leaders following Sora’s public demonstration, noteworthy entertainment-industry figures have actually shown considerable interest in the innovation’s capacity. In an interview, [forum.batman.gainedge.org](https://forum.batman.gainedge.org/index.php?action=profile