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

Released in 2018, Gym Retro is a platform for support learning (RL) research on computer game [147] using RL algorithms and study generalization. Prior RL research study focused mainly on enhancing agents to fix single tasks. Gym Retro offers the ability to generalize between games with comparable ideas however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack knowledge of how to even walk, but are provided the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the agents find out how to adapt to altering conditions. When a representative is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition between representatives might create an intelligence "arms race" that could 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 used in the competitive five-on-five video game Dota 2, that learn to play against human players at a high skill level entirely through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation took place at The International 2017, the yearly best champion tournament for the game, where Dendi, an expert Ukrainian player, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of actual time, which the learning software application was a step in the instructions of creating software that can manage intricate jobs like a surgeon. [152] [153] The system uses a kind of support knowing, as the bots find out with time by playing against themselves numerous 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 ability of the bots expanded to play together as a full group of 5, wavedream.wiki and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, wiki.myamens.com OpenAI Five played in 2 exhibition matches against expert players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs 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 that month, where they played in 42,729 overall 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 obstacles of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has actually demonstrated using deep support knowing (DRL) agents 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 manipulate physical objects. [167] It finds out entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation problem by utilizing domain randomization, a simulation method which exposes the learner to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB electronic cameras to enable the robot to control an arbitrary item 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 demonstrated that Dactyl might resolve a Rubik's Cube. The robot had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complex 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 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 established by OpenAI" to let designers get in touch with it for "any English language AI task". [170] [171]
Text generation

The company 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 showed how a generative model of language might obtain world understanding 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 successor to OpenAI's original GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions initially launched to the public. The full version of GPT-2 was not instantly released due to concern about possible misuse, consisting of applications for composing fake news. [174] Some experts expressed 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 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 launched the total variation of the GPT-2 language design. [177] Several sites host interactive presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose students, highlighted by GPT-2 attaining modern accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the model was not more 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 avoids 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 model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion specifications, [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 couple of as 125 million parameters were likewise 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 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 warned that such scaling-up of language models could be approaching or coming across the fundamental ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous 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 concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month complimentary personal beta that started 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 personal beta. [194] According to OpenAI, larsaluarna.se the model can develop working code in over a dozen programs languages, many successfully in Python. [192]
Several concerns with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of giving off copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would cease assistance 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 technology passed a simulated law school bar test with a score around the top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise read, evaluate or generate up to 25,000 words of text, and compose code in all significant shows languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has declined to reveal numerous technical details and data about GPT-4, such as the exact size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained advanced results in voice, multilingual, and vision benchmarks, 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 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 expects it to be especially helpful for business, startups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have been designed to take more time to think of their actions, causing greater accuracy. These designs are particularly efficient in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning model. OpenAI likewise revealed 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 checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecoms companies O2. [215]
Deep research study

Deep research is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform comprehensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools made it possible for, 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 evaluate the semantic resemblance between text and images. It can notably be utilized for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that creates images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to interpret 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 produce pictures of reasonable things ("a stained-glass window with an image of a blue strawberry") in addition to 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 announced DALL-E 2, an updated version of the design with more reasonable results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new basic system for forum.batman.gainedge.org transforming a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective model much better able to create images from complex descriptions without manual timely engineering and render complicated 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 create videos based on short detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.

Sora's advancement group called it after the Japanese word for "sky", to symbolize its "unlimited creative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that function, but did not expose the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, specifying that it might produce videos up to one minute long. It also shared a technical report highlighting the methods used to train the design, and pipewiki.org the model's abilities. [225] It acknowledged a few of its shortcomings, including struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but kept in mind that they need to have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown considerable interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to create realistic video from text descriptions, citing its prospective to transform storytelling and material creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to stop briefly plans for broadening 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 and is likewise a multi-task model that can carry out multilingual speech acknowledgment along with 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 MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune generated by MuseNet tends to begin fairly however then fall into mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the web psychological 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, the system accepts a category, artist, and a snippet of lyrics and outputs song samples. OpenAI stated the tunes "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge stated "It's technologically outstanding, even if the results sound like mushy versions of tunes that may feel familiar", while Business Insider specified "surprisingly, some of the resulting tunes are memorable 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 purpose is to research study whether such a technique 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 eight neural network models which are often studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various variations 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 provides a conversational interface that enables users to ask concerns in natural language. The system then reacts with a response within seconds.