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 quickly reproducible [24] [144] while providing users with a simple user interface for interacting with these environments. In 2022, brand-new developments of Gym have been transferred 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] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro provides the capability to generalize in between games with comparable ideas however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have understanding of how to even stroll, but are given the objectives of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the representatives discover how to adjust to altering conditions. When a representative is then eliminated from this virtual environment and put in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might develop an intelligence "arms race" that could increase an agent's ability to operate even outside the context of the competitors. [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 players at a high skill level completely through experimental algorithms. Before ending up being a team of 5, the first public demonstration occurred at The International 2017, the yearly premiere champion tournament for the 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 discovered by playing against itself for 2 weeks of genuine time, and that the learning software was a step in the direction of developing software that can handle intricate jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement learning, as the bots find out over time by playing against themselves numerous times a day for months, and are rewarded for forum.pinoo.com.tr 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 complete team of 5, and they were able to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert players, however wound up losing both 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 exhibition match in San Francisco. [163] [164] The bots' last public appearance came later on that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer shows the challenges of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has shown using deep support knowing (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine discovering to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It finds out totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled the object orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB cams to enable the robotic to manipulate an approximate item by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by enhancing the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of producing gradually more difficult environments. ADR varies from manual domain randomization by not needing a human to define randomization ranges. [169]
API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new AI models developed by OpenAI" to let designers get in touch with it for "any English language AI job". [170] [171]
Text generation

The company has promoted 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 composed by Alec Radford and his colleagues, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative model of language could obtain world understanding and procedure 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 follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions at first released to the public. The full version of GPT-2 was not right away released due to issue about prospective abuse, consisting of applications for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 positioned a considerable danger.

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, alerted 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 difficult to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language design. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose students, shown 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 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 utilizing byte pair encoding. This allows 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 specifications, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 designs with as couple of as 125 million parameters were also trained). [186]
OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 significantly enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or coming across the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous 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 design was not right away released to the general public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month free private beta that started 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 released in private beta. [194] According to OpenAI, the design can develop working code in over a dozen programs languages, many effectively in Python. [192]
Several issues with glitches, style flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has actually been accused of producing copyrighted code, without any author attribution or wiki.myamens.com license. [197]
OpenAI revealed that they would cease assistance 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 announced that the upgraded 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 might also read, evaluate or produce 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 model, with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal numerous technical details and stats about GPT-4, such as the precise size of the design. [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 modern lead to voice, multilingual, and vision benchmarks, setting brand-new records in audio speech recognition 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 expects it to be particularly helpful for business, start-ups and designers 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 been designed to take more time to think of their responses, resulting in greater precision. These models 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 changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the follower of the o1 thinking model. OpenAI also revealed o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this model 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 models. [214] The model is called o3 instead of o2 to avoid confusion with telecoms companies O2. [215]
Deep research

Deep research study is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out extensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 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) standard. [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 notably be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that develops images from textual descriptions. [218] DALL-E uses 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 corresponding images. It can create images of practical items ("a stained-glass window with a picture of a blue strawberry") in addition to things that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, oeclub.org 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 realistic results. [219] In December 2022, OpenAI published on GitHub software 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 revealed DALL-E 3, a more effective design much better able to produce images from complex descriptions without manual timely engineering and yewiki.org render intricate 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 design that can create videos based upon short detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora's development team called it after the Japanese word for "sky", to signify its "limitless innovative potential". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that function, but did not reveal the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it could generate videos up to one minute long. It also shared a technical report highlighting the techniques utilized to train the design, and the design's capabilities. [225] It acknowledged a few of its imperfections, including battles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", however noted that they must have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to generate reasonable video from text descriptions, mentioning its possible to revolutionize storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly strategies for broadening his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a big dataset of varied audio and is also a multi-task design that can carry out 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 forecast 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 mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the internet 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 genre, artist, and a bit of lyrics and outputs song samples. OpenAI stated the tunes "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" and that "there is a substantial gap" between Jukebox and human-generated music. The Verge mentioned "It's technically excellent, even if the results sound like mushy variations of songs that may feel familiar", while Business Insider mentioned "remarkably, some of the resulting songs are appealing and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The purpose is to research study whether such a method may help in auditing AI choices and in developing 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 produced to analyze the features that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, different variations of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool constructed on top of GPT-3 that provides a conversational user interface that allows users to ask in natural language. The system then reacts with an answer within seconds.