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

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to fix single tasks. Gym Retro offers the capability to generalize in between 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 stroll, however are provided the objectives of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adjust to changing conditions. When a representative is then gotten rid of from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had learned 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 might increase a representative's capability to function even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, forum.altaycoins.com that discover to play against human players at a high ability level completely through trial-and-error wiki.whenparked.com algorithms. Before ending up being a group of 5, the first public demonstration occurred at The International 2017, the yearly best championship competition for the 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 two weeks of genuine time, which the knowing software application was a step in the instructions of producing software application that can handle complex tasks like a surgeon. [152] [153] The system utilizes a kind of support learning, as the bots learn over time by playing against themselves numerous 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 capability of the bots expanded to play together as a full team of 5, and they had the ability to beat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against professional players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 total video games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot player reveals the challenges of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated making use of deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It learns entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation approach which exposes the student to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cameras to permit the robotic to control 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 demonstrated that Dactyl might solve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating progressively harder environments. ADR differs from manual domain randomization by not requiring a human to varieties. [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 designers contact it for "any English language AI job". [170] [171]
Text generation

The business has actually 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 design was composed by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, yewiki.org 2018. [173] It revealed how a generative design of language could obtain world knowledge and procedure 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 a not being watched transformer language design and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just limited demonstrative versions at first released to the public. The complete variation of GPT-2 was not instantly released due to concern about potential abuse, consisting of applications for writing phony news. [174] Some experts expressed uncertainty that GPT-2 positioned a significant threat.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to absolutely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle 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 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, illustrated by GPT-2 attaining advanced precision 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by using 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 not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI stated 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 full version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were also trained). [186]
OpenAI mentioned 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 knowing between English and Romanian, and in between English and German. [184]
GPT-3 drastically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or encountering the fundamental capability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched to the public for issues of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month totally free private 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, Codex is a descendant of GPT-3 that has actually additionally 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 create working code in over a dozen programs languages, many efficiently in Python. [192]
Several issues with problems, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been accused of emitting copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would discontinue 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), capable of accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar test 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 likewise check out, analyze or generate up to 25,000 words of text, and write code in all major shows 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 caveat that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose different technical details and statistics about GPT-4, such as the exact size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, forum.altaycoins.com and vision criteria, 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 released GPT-4o mini, a smaller sized 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 anticipates it to be particularly helpful for forum.altaycoins.com business, startups and developers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been designed to take more time to think of their reactions, resulting in greater precision. These designs are especially efficient in science, coding, and reasoning jobs, 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 successor bio.rogstecnologia.com.br 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 model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecoms providers O2. [215]
Deep research

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out comprehensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, it reached an accuracy 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 resemblance between text and images. It can especially be utilized 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 shaped like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can create images of sensible items ("a stained-glass window with a picture of a blue strawberry") in addition to 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 announced DALL-E 2, an upgraded variation of the design with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-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, archmageriseswiki.com a more powerful model better able to generate images from complex descriptions without manual timely engineering and render complicated 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 in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of generated videos is unidentified.

Sora's advancement group called it after the Japanese word for "sky", to represent its "limitless creative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that purpose, however did not reveal the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could create videos approximately one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the design's abilities. [225] It acknowledged a few of its shortcomings, including struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted 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 demonstration, significant entertainment-industry figures have shown significant interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to produce realistic video from text descriptions, citing its prospective to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly plans for broadening his Atlanta-based film 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 likewise a multi-task model that can carry out multilingual speech acknowledgment along with speech translation and language recognition. [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 create songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly however then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet mental thriller Ben Drowned to develop 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 snippet of lyrics and outputs tune samples. OpenAI specified the songs "reveal regional musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes do not have "familiar bigger 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 technologically excellent, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches makers to discuss toy issues in front of a human judge. The purpose is to research whether such a technique 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 significant layer and nerve cell of eight neural network models which are often studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool built 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.