Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research, making released research study more quickly reproducible [24] [144] while providing users with a basic interface for communicating with these environments. In 2022, brand-new developments of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to fix single jobs. Gym Retro provides the capability to generalize in between games with comparable ideas however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first lack knowledge of how to even stroll, however are given the goals of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adapt to altering conditions. When a representative is then removed from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might produce an intelligence "arms race" that might increase an agent's ability to function even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high ability level entirely through experimental algorithms. Before ending up being a team of 5, the first public presentation took place at The International 2017, the yearly best championship competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of actual time, and that the knowing software application was a step in the instructions of producing software application that can manage complex tasks like a cosmetic surgeon. [152] [153] The system uses a kind of reinforcement learning, as the bots find out with time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a complete team of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional gamers, but ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 overall 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 gamer shows the obstacles of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown the usage of deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It learns entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB cams to permit the robotic to control an approximate things 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 solve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of producing progressively harder environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]
API

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

The company 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 design was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world understanding and procedure long-range reliances by pre-training on a varied 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 initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative variations at first released to the general public. The full version of GPT-2 was not immediately launched due to issue about possible abuse, consisting of applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 positioned a substantial risk.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to find "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete variation 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 unsupervised language models to be general-purpose students, illustrated by GPT-2 attaining advanced precision 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 prevents certain issues encoding vocabulary with word tokens by using 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 not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of 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 gave examples of translation and cross-linguistic transfer learning in between English and Romanian, and in between English and German. [184]
GPT-3 considerably enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or wiki.dulovic.tech experiencing the basic capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, 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 produce working code in over a dozen programming languages, most successfully in Python. [192]
Several issues with problems, design defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been accused of giving off copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would discontinue 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), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar test with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also read, evaluate or produce up to 25,000 words of text, and compose code in all major programming languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the issues with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to reveal various technical details and it-viking.ch statistics about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and engel-und-waisen.de generate text, images and photorum.eclat-mauve.fr audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision benchmarks, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller version of GPT-4o replacing 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 useful for business, start-ups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been created to take more time to consider their responses, leading to greater precision. These designs are particularly reliable in science, coding, and reasoning jobs, 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 model. OpenAI likewise unveiled o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the opportunity to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]
Deep research

Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, 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 model 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 produces 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 bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and pediascape.science produce matching images. It can develop pictures of practical things ("a stained-glass window with an image of a blue strawberry") as well as 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 upgraded version of the design with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new primary system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

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

Sora

Sora is a text-to-video model that can produce videos based on short detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.

Sora's development team named it after the Japanese word for "sky", to signify its "endless creative capacity". [223] Sora's innovation is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that function, however did not reveal the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it might create videos up to one minute long. It likewise shared a technical report highlighting the approaches used to train the model, and the design's capabilities. [225] It acknowledged a few of its shortcomings, consisting of battles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but kept in mind that they need to have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have revealed substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's ability to generate realistic video from text descriptions, citing its prospective to reinvent storytelling and content development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause prepare for broadening his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, engel-und-waisen.de Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a big dataset of varied audio and is likewise a multi-task design that can perform 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 anticipate subsequent musical notes in MIDI music files. It can create tunes with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall into the longer it plays. [230] [231] In popular 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 snippet of lyrics and outputs tune samples. OpenAI specified the songs "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable space" between Jukebox and human-generated music. The Verge stated "It's highly remarkable, even if the outcomes sound like mushy variations of songs that may feel familiar", while Business Insider mentioned "surprisingly, some of the resulting songs are catchy and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched 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 method might 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 considerable layer and neuron of eight neural network models which are typically studied in interpretability. [240] Microscope was created to examine the functions that form inside these neural networks quickly. 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 developed on top of GPT-3 that supplies a conversational user interface that enables users to ask questions in natural language. The system then reacts with an answer within seconds.