Announced in 2016, wiki.whenparked.com Gym is an open-source Python library developed to facilitate the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, making published research more easily reproducible [24] [144] while offering users with a simple interface for interacting with these environments. In 2022, new advancements of Gym have actually been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to fix single tasks. Gym Retro gives the ability to generalize in between video games with comparable concepts but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially do not have knowledge of how to even stroll, but are provided the goals of finding out 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 put in a new virtual environment with high winds, the agent braces to remain upright, recommending it had discovered how to stabilize in a generalized method. [148] [149] OpenAI’s Igor Mordatch argued that competition between representatives could develop an intelligence “arms race” that might increase an agent’s capability to operate 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 computer game Dota 2, that discover to play against human players at a high ability level entirely through experimental algorithms. Before ending up being a team of 5, the first public demonstration happened at The International 2017, the yearly best championship competition for the video game, where Dendi, a professional Ukrainian player, 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 2 weeks of genuine time, which the knowing software application was a step in the instructions of creating software that can handle complex tasks like a surgeon. [152] [153] The system utilizes a kind of reinforcement learning, as the bots discover with 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 goals. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a complete team of 5, and they had the ability to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert gamers, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots’ final 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 video games. [165]
OpenAI 5’s systems in Dota 2’s bot player reveals the challenges of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated using deep support knowing (DRL) agents to attain superhuman proficiency 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 items. [167] It finds out entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by using domain randomization, a simulation method which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB video cameras to permit the robotic to control 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 showed that Dactyl could resolve a Rubik’s Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik’s Cube introduce complex physics that is harder to model. OpenAI did this by improving the toughness 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 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 business has actually promoted generative pretrained transformers (GPT). [172]
OpenAI’s original GPT model (“GPT-1”)

The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his coworkers, and released in preprint on OpenAI’s website on June 11, 2018. [173] It revealed how a generative model of language might obtain world knowledge and procedure long-range dependences 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 only limited demonstrative versions at first launched to the public. The full version of GPT-2 was not immediately launched due to issue about possible misuse, consisting of applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 postured a substantial risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to find “neural phony news”. [175] Other researchers, such as Jeremy Howard, cautioned of “the technology to completely 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 version of the GPT-2 language model. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2’s authors argue not being watched language designs to be general-purpose learners, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (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 prevents certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 design and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion parameters, [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 specifications were also trained). [186]
OpenAI mentioned that GPT-3 succeeded at certain “meta-learning” jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184]
GPT-3 considerably improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or experiencing the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of calculate, compared to 10s 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 planned to enable gain access to through a paid cloud API after a two-month complimentary private 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 launched in private beta. [194] According to OpenAI, the design can develop working code in over a dozen programs languages, many efficiently in Python. [192]
Several concerns with glitches, style flaws and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of giving off copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would stop 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 innovation passed a simulated law school bar test with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, evaluate or create as much as 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained some of the problems with earlier modifications. [201] GPT-4 is likewise 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 accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, setiathome.berkeley.edu images and audio. [204] GPT-4o attained modern outcomes in 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) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller version of GPT-4o replacing 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 especially beneficial for enterprises, start-ups and designers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been designed to take more time to believe about their actions, causing higher accuracy. These models are especially effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and quicker version of OpenAI o3. As of December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, setiathome.berkeley.edu 2025, security and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecoms providers 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 substantial web surfing, data analysis, and synthesis, providing 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) benchmark. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic resemblance in between text and images. It can especially be used for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that develops images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as “a green leather purse shaped like a pentagon” or “an isometric view of an unfortunate capybara”) and create corresponding images. It can create pictures of practical things (“a stained-glass window with an image of a blue strawberry”) as well as objects that do not exist in truth (“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 practical results. [219] In December 2022, OpenAI released on GitHub software application for surgiteams.com Point-E, a brand-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 much better able to produce images from complicated 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 produce videos based on short detailed prompts [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 created videos is unidentified.

Sora’s development team named it after the Japanese word for “sky”, to signify its “unlimited innovative potential”. [223] Sora’s technology is an adaptation of the innovation 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 purpose, but did not reveal the number or the precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could generate videos approximately one minute long. It also shared a technical report highlighting the methods used to train the model, and setiathome.berkeley.edu the design’s abilities. [225] It acknowledged some of its drawbacks, including struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos “excellent”, but noted that they must have been cherry-picked and might not represent Sora’s typical output. [225]
Despite uncertainty from some scholastic leaders following Sora’s public demonstration, noteworthy entertainment-industry figures have revealed substantial interest in the innovation’s capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation’s capability to produce reasonable video from text descriptions, mentioning its possible to reinvent storytelling and content development. He said that his excitement about Sora’s possibilities was so strong that he had decided 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 recognition design. [228] It is trained on a large dataset of diverse audio and is likewise a multi-task model that can carry out multilingual speech recognition in addition to speech translation and language identification. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to predict subsequent musical notes in files. It can generate tunes with 10 instruments in 15 designs. According to The Verge, a song generated by MuseNet tends to start fairly however then fall under mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to develop 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 stated the songs “show regional musical coherence [and] follow standard chord patterns” but acknowledged that the songs lack “familiar larger musical structures such as choruses that repeat” which “there is a considerable space” in between Jukebox and human-generated music. The Verge specified “It’s technologically outstanding, even if the outcomes sound like mushy variations of songs that might feel familiar”, while Business Insider stated “remarkably, a few of the resulting songs are appealing and sound legitimate”. [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches machines to debate toy issues in front of a human judge. The function is to research study whether such a method might assist in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope

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

Launched in November 2022, ChatGPT is a synthetic intelligence tool built on top of GPT-3 that offers a conversational interface that permits users to ask concerns in natural language. The system then responds with a response within seconds.