Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of support knowing algorithms. It aimed to standardize how environments are defined in AI research study, making released research more quickly reproducible [24] [144] while providing users with a basic interface for connecting with these environments. In 2022, new advancements of Gym have been moved 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] using RL algorithms and study generalization. Prior RL research focused mainly on optimizing agents to resolve single jobs. Gym Retro gives the capability to generalize between games with comparable ideas however various appearances.

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, however are offered the goals of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to altering conditions. When an agent is then eliminated from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, suggesting it had learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between agents could develop an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that learn to play against human gamers at a high ability level completely through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation 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 one-on-one 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 genuine time, which the learning software was a step in the direction of producing software that can deal with complicated jobs like a surgeon. [152] [153] The system utilizes a form of reinforcement knowing, 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 and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots broadened to play together as a complete group of 5, and they were able to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibit matches against expert players, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated 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 on 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 shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown making use of deep reinforcement learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, wiki.myamens.com Dactyl uses machine finding out to train a Shadow Hand, a human-like robotic hand, to manipulate physical objects. [167] It learns totally in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences instead of attempting to fit to truth. The set-up for Dactyl, wiki.vst.hs-furtwangen.de aside from having movement tracking video cameras, likewise has RGB video cameras to allow the robot to manipulate an approximate object 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 could fix a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of generating gradually more difficult environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

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

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

The original paper on generative pre-training of a transformer-based language design was written by Alec Radford and forum.batman.gainedge.org 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 knowledge and process long-range dependencies by pre-training on a diverse 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 announced in February 2019, with just restricted demonstrative variations initially released to the general public. The complete variation of GPT-2 was not instantly released due to concern about possible abuse, including applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 presented a significant risk.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "neural phony news". [175] Other scientists, such as Jeremy Howard, warned of "the technology 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 launched the total version of the GPT-2 language design. [177] Several websites 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 learners, highlighted by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not further 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 concerns 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 without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated 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 version of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were likewise trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs and might generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning in between English and systemcheck-wiki.de Romanian, and between English and German. [184]
GPT-3 considerably improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or coming across the basic 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 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately released to the general public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month complimentary private beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has 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 programs languages, many efficiently in Python. [192]
Several concerns with glitches, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been accused of emitting copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would discontinue 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 top 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 might also check out, examine or create as much as 25,000 words of text, and write 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 iteration, with the caution that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has declined to expose various technical details and data about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision standards, setting brand-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 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 beneficial for enterprises, startups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been created to take more time to believe about their actions, causing higher precision. These designs are especially efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Staff member. [209] [210] In December 2024, raovatonline.org o1-preview was changed by o1. [211]
o3

On December 20, 2024, OpenAI revealed o3, the follower of the o1 reasoning design. OpenAI also unveiled o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this model is not available for public use. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these designs. [214] The design is called o3 rather than o2 to prevent confusion with telecoms providers O2. [215]
Deep research study

Deep research is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform comprehensive web browsing, data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to analyze 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 model 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 formed like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can produce pictures of practical objects ("a stained-glass window with a picture of a blue strawberry") in addition to 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 model with more sensible results. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new rudimentary system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more powerful design much better able to create images from intricate descriptions without manual timely engineering and render complicated details like hands and text. [221] It was launched 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 upon short detailed triggers [223] in addition to extend existing videos forwards or in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.

Sora's advancement group called it after the Japanese word for "sky", to signify its "endless creative potential". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [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 exact 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 also shared a technical report highlighting the methods utilized to train the design, and the model's abilities. [225] It acknowledged some of its shortcomings, consisting of struggles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", but kept in mind that they must have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have revealed significant interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's ability to produce reasonable video from text descriptions, citing its prospective to reinvent storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly plans for expanding his Atlanta-based movie 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 perform 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 create tunes with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly however then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the web psychological thriller Ben Drowned to develop music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to create 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 mentioned the songs "reveal regional musical coherence [and] follow traditional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that repeat" and that "there is a considerable gap" between Jukebox and human-generated music. The Verge specified "It's technically impressive, even if the outcomes seem like mushy variations of songs that might feel familiar", while Business Insider stated "surprisingly, a few of the resulting songs are appealing and sound genuine". [234] [235] [236]
User interfaces

Debate Game

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

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and nerve cell of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks easily. The designs included 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 user interface that permits users to ask concerns in natural language. The system then responds with an answer within seconds.