Announced in 2016, Gym is an open-source Python library created to help with the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research study, making released research more easily reproducible [24] [144] while offering users with an easy user interface for connecting with these environments. In 2022, brand-new advancements 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 computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to resolve single jobs. Gym Retro offers the ability to generalize between games with comparable ideas but various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first lack understanding of how to even walk, however are provided the objectives of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to changing conditions. When a representative is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might produce an intelligence "arms race" that might increase an agent's ability to work 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 computer game Dota 2, that learn to play against human players at a high skill level totally through experimental algorithms. Before ending up being a team of 5, the first public demonstration took place at The International 2017, the annual best championship competition for the video game, where Dendi, a professional Ukrainian gamer, 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 two weeks of actual time, and that the knowing software application was a step in the instructions of producing software that can deal with complex jobs like a surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots learn over 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 objectives. [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 defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert gamers, 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 exhibit match in San Francisco. [163] [164] The bots' last public look came later 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 gamer reveals the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown making use of deep support knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It discovers totally in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB cameras to permit the robot 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 demonstrated that Dactyl might resolve a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to design. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of producing gradually harder environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new AI designs established by OpenAI" to let designers contact it for "any English language AI task". [170] [171]
Text generation

The business has actually 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 written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language could obtain world understanding and process long-range dependences by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative 2 ("GPT-2") is a not being watched transformer language design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just restricted demonstrative variations initially released to the general public. The full variation of GPT-2 was not instantly launched due to issue about possible misuse, consisting of applications for composing phony news. [174] Some professionals expressed uncertainty that GPT-2 positioned a considerable risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted of "the technology to totally 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 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 not being watched language models to be general-purpose students, highlighted by GPT-2 attaining modern 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns 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 not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI specified that the full version of GPT-3 contained 175 billion criteria, [184] 2 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 criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" jobs and could 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 considerably improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of 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 immediately released to the general public for issues of possible abuse, although OpenAI prepared 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 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 launched in private beta. [194] According to OpenAI, the model can create working code in over a lots programming languages, most effectively in Python. [192]
Several problems with problems, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of discharging 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), capable of accepting text or image inputs. [199] They announced that the updated technology passed a simulated law school bar exam with a rating around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, analyze or produce up to 25,000 words of text, and compose code in all significant programming languages. [200]
Observers reported that the iteration of ChatGPT using 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 revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal different technical details and statistics about GPT-4, such as the exact size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision criteria, setting new records in audio speech acknowledgment 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 sized 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 anticipates it to be particularly helpful for enterprises, startups and developers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been created to take more time to think about their actions, leading to greater precision. These designs are especially effective in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Team members. [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 also unveiled 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 checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]
Deep research study

Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform comprehensive web surfing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, 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 model that is trained to examine the semantic similarity 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 model that develops images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter version of GPT-3 to translate natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of a sad capybara") and generate matching images. It can create pictures of practical objects ("a stained-glass window with a picture of a blue strawberry") along with things 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 announced DALL-E 2, an upgraded version of the design with more practical outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new fundamental system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

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

Sora

Sora is a text-to-video model that can generate videos based on short detailed triggers [223] along with 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 advancement team named it after the Japanese word for "sky", to symbolize its "unlimited imaginative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as 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 general public on February 15, 2024, stating that it might create videos as much as one minute long. It likewise shared a technical report highlighting the methods used to train the design, and the model's abilities. [225] It acknowledged a few of its imperfections, including battles simulating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they should have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to create realistic video from text descriptions, mentioning its potential to change storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause strategies for expanding his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [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 as well as speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to predict 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 start fairly but then fall into turmoil the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to create 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, larsaluarna.se the system accepts a category, artist, and gratisafhalen.be a bit of lyrics and outputs tune samples. OpenAI specified the songs "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a substantial gap" in between Jukebox and human-generated music. The Verge mentioned "It's technically excellent, even if the results seem like mushy versions of songs that might feel familiar", while Business Insider mentioned "surprisingly, a few of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches devices to dispute 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 significant layer and neuron of 8 neural network designs which are typically studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different variations of Inception, and various versions 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 user interface that permits users to ask questions in natural language. The system then reacts with a response within seconds.