Announced in 2016, Gym is an open-source Python library developed to assist in the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making released research more quickly reproducible [24] [144] while offering users with an easy interface for communicating with these environments. In 2022, new advancements of Gym have actually been transferred 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] using RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to solve single tasks. Gym Retro provides the capability to generalize between video games with comparable principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially do not have understanding of how to even stroll, however are provided the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the representatives discover how to adapt to changing conditions. When an agent is then eliminated from this virtual environment and put in a 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 representatives could create an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competitors. [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 learn to play against human players at a high ability level totally through experimental algorithms. Before becoming a team of 5, the very first public demonstration occurred at The International 2017, the yearly premiere championship tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for two weeks of actual time, and that the knowing software was a step in the instructions of creating software application that can manage complex tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots learn gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a full group 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 expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling 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 appearance came later that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the challenges of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown making use of deep support knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine learning to train a Shadow Hand, a human-like robotic hand, to control physical things. [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 utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB video cameras to enable the robot to control an approximate things 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 might solve a Rubik's Cube. The robot had the ability to fix 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 robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of producing progressively harder environments. ADR varies from manual domain randomization by not requiring a human to define randomization varieties. [169]
API

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

The company has popularized 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 colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding 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 an unsupervised transformer language model and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative versions initially launched to the general public. The complete variation of GPT-2 was not instantly launched due to issue about potential abuse, including applications for writing phony news. [174] Some professionals expressed uncertainty that GPT-2 postured a significant danger.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, cautioned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI released the complete variation of the GPT-2 language design. [177] Several sites host interactive presentations of various instances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose students, illustrated by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not additional 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 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 a not being watched transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the full variation of GPT-3 contained 175 billion parameters, [184] two 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 specifications were likewise trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the purpose of a single input-output pair. The GPT-3 release paper offered 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 warned that such scaling-up of language models might be approaching or encountering the essential ability constraints of predictive language models. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away launched 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 personal 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 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 launched in private beta. [194] According to OpenAI, the design can produce working code in over a dozen programming languages, many efficiently in Python. [192]
Several issues with problems, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been accused of producing copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would stop 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 revealed that the upgraded technology passed a simulated law school bar exam 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 check out, evaluate or create up to 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the iteration 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 issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various and stats about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained state-of-the-art results in 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) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller variation 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 particularly useful for business, start-ups and developers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been created to take more time to believe about their reactions, causing greater accuracy. These models are particularly effective in science, yewiki.org coding, and thinking 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 revealed o3, the follower of the o1 thinking design. OpenAI also unveiled o3-mini, a lighter and faster version of OpenAI o3. As of December 21, 2024, this design is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications services provider O2. [215]
Deep research

Deep research study is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform extensive web browsing, information analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools enabled, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze the semantic similarity 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 develops 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 bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce matching images. It can develop pictures of sensible items ("a stained-glass window with a picture of a blue strawberry") in addition to items 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 variation of the model with more practical results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new simple system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, wiki.myamens.com a more powerful design better able to produce images from complicated descriptions without manual prompt engineering and render complicated details like hands and text. [221] It was launched to the general public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can create videos based on brief detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.

Sora's development group called it after the Japanese word for "sky", to represent its "endless creative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that function, however did not expose the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it could create videos as much as one minute long. It likewise shared a technical report highlighting the techniques utilized to train the model, and the design's capabilities. [225] It acknowledged some of its shortcomings, including battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", however kept in mind that they should have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have revealed considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to create sensible video from text descriptions, mentioning its possible to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause prepare for expanding his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a big dataset of varied audio and is also a multi-task model that can carry out multilingual speech acknowledgment as well as speech translation and language recognition. [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 generate songs with 10 instruments in 15 styles. According to The Verge, a song created by MuseNet tends to begin 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 produce 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 mentioned the songs "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that duplicate" which "there is a substantial space" in between Jukebox and human-generated music. The Verge stated "It's highly remarkable, even if the outcomes seem like mushy variations of tunes that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to dispute toy issues in front of a human judge. The purpose is to research whether such a method may help in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope

Released in 2020, bytes-the-dust.com Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network models which are often studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various versions of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an expert system tool constructed on top of GPT-3 that supplies a conversational interface that enables users to ask questions in natural language. The system then responds with a response within seconds.