Announced in 2016, Gym is an open-source Python library created to help with the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making published research more quickly reproducible [24] [144] while supplying users with a simple user interface for connecting with these environments. In 2022, brand-new developments of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

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

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have understanding of how to even stroll, but are given the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this process, the agents find out how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could develop an intelligence "arms race" that could increase a representative's ability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that learn to play against human gamers at a high ability level entirely through trial-and-error algorithms. Before ending up being a group of 5, the first public demonstration occurred at The International 2017, the yearly best champion tournament 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 discovered by playing against itself for genbecle.com 2 weeks of actual time, which the knowing software application was a step in the instructions of producing software application that can deal with complicated jobs like a cosmetic surgeon. [152] [153] The system utilizes a form of support knowing, as the bots discover 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 capability of the bots expanded to play together as a full group of 5, and they had the ability to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, but wound up losing both 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' last public appearance came later on that month, where they played in 42,729 overall video games in a four-day open online competitors, winning 99.4% of those 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 actually shown using deep reinforcement learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It learns completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI tackled 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 reality. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB cams to enable the robot to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating progressively harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

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

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

The initial paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his coworkers, and published 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 dependencies by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions initially released to the general public. The complete version of GPT-2 was not immediately released due to concern about possible abuse, including applications for composing fake news. [174] Some professionals revealed uncertainty that GPT-2 presented a considerable threat.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the innovation to absolutely 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 websites host interactive presentations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose learners, shown by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains a little 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 using byte pair encoding. This allows 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 an unsupervised transformer language design and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete 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 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 knowing 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 models might be approaching or coming across the essential capability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of calculate, compared to 10s 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 general public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month totally free 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 released in private beta. [194] According to OpenAI, the design can produce working code in over a lots programming languages, the majority of effectively in Python. [192]
Several issues with problems, design defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been implicated of giving off copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would cease 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 announced that the upgraded technology passed a simulated law school bar test 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 could likewise read, evaluate or produce up to 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the model 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 revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose different technical details and statistics about GPT-4, such as the precise size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and wavedream.wiki create text, images and audio. [204] GPT-4o attained state-of-the-art 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 variation 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 anticipates it to be particularly beneficial for enterprises, startups and designers 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 developed to take more time to think about their reactions, causing greater accuracy. These designs are especially reliable in science, coding, and reasoning 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 quicker version of OpenAI o3. As of 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 scientists had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to avoid confusion with telecoms services company O2. [215]
Deep research study

Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform substantial web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools made it possible for, it reached a precision of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
Image category

CLIP

Revealed in 2021, systemcheck-wiki.de CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic similarity between text and images. It can especially 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 uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can create images of practical objects ("a stained-glass window with a picture of a blue strawberry") along with things that do not exist in reality ("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 variation of the design with more realistic results. [219] In December 2022, OpenAI released on GitHub software 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 announced DALL-E 3, a more powerful model much better able to generate images from intricate descriptions without manual prompt engineering and render complicated 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 generate videos based upon brief detailed triggers [223] along with extend existing videos forwards or in reverse in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's development group named it after the Japanese word for "sky", to represent its "limitless creative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with 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 general public on February 15, 2024, specifying that it might generate videos as much as one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the model's capabilities. [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 "excellent", however kept in mind that they should have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the innovation's ability to produce practical video from text descriptions, citing its prospective to change storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to pause prepare for broadening 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 big dataset of varied audio and is likewise a multi-task design 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 generate songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall under turmoil 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 genre, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the songs "reveal local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" which "there is a significant gap" between Jukebox and human-generated music. The Verge stated "It's technologically impressive, even if the results sound like mushy versions of tunes that may feel familiar", while Business Insider mentioned "surprisingly, some of the resulting songs are appealing and sound genuine". [234] [235] [236]
Interface

Debate Game

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

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

Launched in November 2022, ChatGPT is an expert system tool developed on top of GPT-3 that offers a conversational interface that permits users to ask questions in natural language. The system then reacts with an answer within seconds.