Announced in 2016, Gym is an open-source Python library designed to assist in the development of support learning algorithms. It aimed to standardize how environments are defined in AI research study, making published research study more easily reproducible [24] [144] while offering users with a simple user interface for engaging with these environments. In 2022, brand-new advancements of Gym have been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support knowing (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to solve single tasks. Gym Retro provides the capability to generalize between video games with similar ideas but different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack knowledge of how to even stroll, kigalilife.co.rw but are provided 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 discover how to adapt to altering 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 learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents might create an intelligence "arms race" that could increase an agent's capability 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 ability level completely through experimental algorithms. Before becoming a team of 5, the first public demonstration took place at The International 2017, the annual premiere champion competition for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for 2 weeks of actual time, which the learning software application was an action in the direction of developing software application that can handle complex tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of support learning, as the bots learn with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent 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 were able to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs 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 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 mechanisms 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 actually shown using deep support knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It learns completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by using domain randomization, a simulation technique which exposes the student to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB video cameras to allow the robotic to control an arbitrary things by seeing it. In 2018, OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of creating gradually harder environments. ADR varies from manual domain randomization by not requiring a human to specify 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 contact it for "any English language AI job". [170] [171]
Text generation

The business has promoted generative pretrained transformers (GPT). [172]
OpenAI's original GPT design ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language could obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language model and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just minimal demonstrative versions initially released to the general public. The complete variation of GPT-2 was not right away released due to concern about possible abuse, consisting of applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 presented a significant hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the innovation to completely 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 total version of the GPT-2 language model. [177] Several sites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose students, illustrated by GPT-2 attaining advanced accuracy 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by using 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 model and the successor to GPT-2. [182] [183] [184] OpenAI specified 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 variation of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were also trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" tasks and might generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer knowing in between English and Romanian, and in between English and German. [184]
GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or encountering the basic ability constraints of predictive language models. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the public for concerns of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month free private beta that started 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 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 personal beta. [194] According to OpenAI, the design can create working code in over a lots programs languages, most effectively in Python. [192]
Several problems with problems, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been implicated of giving off copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would terminate 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), efficient in accepting text or image inputs. [199] They revealed that the upgraded innovation 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 might likewise check out, evaluate or create approximately 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the problems with earlier revisions. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal numerous technical details and data about GPT-4, such as the precise 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 cutting edge results in voice, multilingual, and vision benchmarks, setting brand-new records in audio speech acknowledgment 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 launched GPT-4o mini, a smaller version of GPT-4o changing 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 enterprises, startups and designers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been created to take more time to consider their responses, causing greater accuracy. These models are particularly reliable in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are evaluating 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 design is called o3 rather than o2 to avoid confusion with telecommunications services provider O2. [215]
Deep research study

Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out substantial web browsing, data 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) criteria. [120]
Image classification

CLIP

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 notably 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 version 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 produce matching images. It can develop pictures of practical objects ("a stained-glass window with a picture of a blue strawberry") as well as 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 updated version of the model with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a new basic system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

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

Sora

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

Sora's advancement team called it after the Japanese word for "sky", to signify its "endless imaginative capacity". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos licensed for that purpose, but did not expose the number or the precise sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, mentioning that it might create videos approximately one minute long. It likewise shared a technical report highlighting the methods utilized to train the design, and the design's abilities. [225] It acknowledged some of its shortcomings, including struggles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however kept in mind that they need to have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have shown considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to generate sensible video from text descriptions, citing its prospective to transform storytelling and material production. He said that his enjoyment 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 recognition design. [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 along with 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 designs. According to The Verge, a tune produced by MuseNet tends to start fairly however then fall into mayhem the longer it plays. [230] [231] In pop culture, preliminary 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 produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs song samples. OpenAI specified the tunes "show regional musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge mentioned "It's technologically outstanding, even if the outcomes sound like mushy versions of tunes that may 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 function is to research whether such a technique might help in auditing AI choices and in establishing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network models which are typically studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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