GitLab

Announced in 2016, Gym is an open-source Python library designed to assist in the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research study, making published research more easily reproducible [24] [144] while offering users with a simple interface for engaging with these environments. In 2022, brand-new advancements of Gym have actually been relocated 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 research study generalization. Prior RL research study focused mainly on optimizing agents to fix single tasks. Gym Retro offers the ability to generalize in between video games with comparable principles however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first do not have knowledge of how to even walk, however are offered the goals of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adjust to changing conditions. When an agent is then gotten rid of from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, recommending it had actually found out how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might 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 utilized in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high ability level entirely through trial-and-error algorithms. Before becoming a group of 5, the very first public demonstration happened at The International 2017, the yearly premiere championship tournament 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 found out by playing against itself for 2 weeks of actual time, which the knowing software was a step in the instructions of producing software application that can handle complicated jobs like a surgeon. [152] [153] The system utilizes a form of support learning, as the bots discover over time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full group 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 professional gamers, but ended up losing both 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 exhibit match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot player reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has demonstrated using deep support learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, yewiki.org Dactyl utilizes machine discovering to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It finds out totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB video cameras to enable the robotic to manipulate an approximate 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 showed that Dactyl could resolve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of producing gradually more hard environments. ADR varies from manual domain randomization by not needing a human to specify randomization ranges. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing 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 popularized generative pretrained transformers (GPT). [172]
OpenAI's original GPT model ("GPT-1")

The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and process 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 an unsupervised transformer language model and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions initially launched to the general public. The full variation of GPT-2 was not immediately released due to issue about possible misuse, including applications for composing phony news. [174] Some professionals revealed uncertainty that GPT-2 postured a significant hazard.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural fake news". [175] Other scientists, such as Jeremy Howard, cautioned of "the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush 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 websites host interactive demonstrations 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, shown by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design 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 prevents 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 a not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full variation of GPT-2 (although GPT-3 models with as few as 125 million parameters were likewise trained). [186]
OpenAI stated 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 provided examples of translation and cross-linguistic transfer knowing between English and Romanian, and photorum.eclat-mauve.fr in between English and German. [184]
GPT-3 significantly improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or coming across the basic capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the general public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month totally free personal beta that began 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 furthermore 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 develop working code in over a lots programs languages, most successfully in Python. [192]
Several concerns with glitches, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been implicated of releasing copyrighted code, with no author attribution or license. [197]
OpenAI revealed 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), 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 also read, examine or generate approximately 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an improvement on the previous GPT-3.5-based version, 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 declined to expose numerous technical details and data 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 text, images and audio. [204] GPT-4o attained state-of-the-art 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) criteria compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched 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 expects it to be especially useful for enterprises, start-ups and designers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been designed to take more time to think about their actions, leading to greater precision. These models are particularly reliable in science, coding, and thinking jobs, 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 unveiled o3, the follower of the o1 reasoning model. OpenAI also revealed o3-mini, a lighter and faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these designs. [214] The design is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
Deep research

Deep research study is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform substantial web browsing, information 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) benchmark. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic resemblance in between text and images. It can significantly be utilized for image classification. [217]
Text-to-image

DALL-E

Revealed in 2021, wiki.myamens.com DALL-E is a Transformer design that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to translate natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and create corresponding images. It can create pictures of realistic objects ("a stained-glass window with a picture of a blue strawberry") along with objects that do not exist in reality ("a cube with the texture of a porcupine"). As of March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an upgraded variation of the model with more sensible outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new rudimentary system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

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

Sora

Sora is a text-to-video design that can produce videos based upon short detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can produce videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unidentified.

Sora's advancement team called it after the Japanese word for "sky", to symbolize its "unlimited innovative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos along with copyrighted videos accredited for wiki.vst.hs-furtwangen.de that purpose, but did not reveal the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it might create videos up to one minute long. It likewise shared a technical report highlighting the approaches used to train the design, wiki.vst.hs-furtwangen.de and wiki.dulovic.tech the model's capabilities. [225] It acknowledged some of its drawbacks, consisting of struggles replicating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", but noted that they need to have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's ability to generate realistic video from text descriptions, mentioning its prospective to transform storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to stop briefly strategies for expanding his Atlanta-based film 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 acknowledgment in addition to speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to forecast subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized 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 create music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a snippet of lyrics and outputs tune samples. OpenAI specified the songs "reveal regional musical coherence [and] follow conventional chord patterns" however acknowledged that the songs lack "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable space" in between Jukebox and human-generated music. The Verge mentioned "It's technically outstanding, even if the outcomes sound like mushy variations of songs that may feel familiar", disgaeawiki.info while Business Insider stated "remarkably, a few of the resulting songs are memorable and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI launched the Debate Game, which teaches devices to dispute toy problems in front of a human judge. The function is to research study whether such a technique may 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 nerve cell of 8 neural network models which are typically studied in interpretability. [240] Microscope was developed to evaluate the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different 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 allows users to ask questions in natural language. The system then responds with a response within seconds.