boergmann

Announced in 2016, Gym is an open-source Python library designed to facilitate the advancement of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research study, making published research more easily reproducible [24] [144] while providing users with a basic user interface for communicating with these environments. In 2022, 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 study on video games [147] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to solve single jobs. Gym Retro gives the ability to generalize in between games with similar ideas however different 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 walk, however are given the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adjust to altering conditions. When a representative is then removed from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, recommending it had discovered how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could produce an intelligence "arms race" that might increase an agent's ability to work even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a group of 5 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 totally through experimental algorithms. Before ending up being a team of 5, the very first public demonstration happened at The International 2017, the annual premiere champion tournament for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually discovered by playing against itself for two weeks of real time, and that the learning software was an action in the direction of developing software application that can handle intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a form of support learning, as the bots learn over time by playing against themselves hundreds of 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 capability of the bots expanded to play together as a complete team of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against expert players, but ended up losing both 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 exhibition match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 total 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 gamer shows the challenges of AI systems in multiplayer online fight arena (MOBA) games and how OpenAI Five has shown the usage of deep reinforcement learning (DRL) representatives to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses maker discovering to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It learns entirely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation problem by using domain randomization, a simulation approach which exposes the student to a variety of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB cameras to allow the robot to control an approximate things by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could solve a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of producing gradually harder environments. ADR differs 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 designs established by OpenAI" to let developers get in touch with it for "any English language AI job". [170] [171]
Text generation

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

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It revealed how a generative design 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 not being watched transformer language design and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions at first released to the general public. The full version of GPT-2 was not instantly launched due to concern about potential abuse, consisting of applications for writing fake news. [174] Some experts expressed uncertainty that GPT-2 positioned a considerable hazard.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural fake 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 hush all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language model. [177] Several websites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language models to be general-purpose learners, highlighted by GPT-2 attaining state-of-the-art 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 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 individual 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 successor to GPT-2. [182] [183] [184] OpenAI stated that the full version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 models with as few as 125 million criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" jobs and could generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer knowing between and Romanian, and between English and German. [184]
GPT-3 considerably improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or encountering the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 needed several 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 immediately released to the general public for issues of possible abuse, although OpenAI prepared to enable gain access to through a paid cloud API after a two-month totally free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed solely 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 programming languages, most successfully in Python. [192]
Several problems with problems, style flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of discharging copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would discontinue assistance for Codex API on March 23, 2023. [198]
GPT-4

On March 14, 2023, OpenAI revealed the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They revealed that the upgraded innovation 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 generate approximately 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, wiki.asexuality.org with the caution that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased 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, wakewiki.de OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge lead to 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 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 especially helpful for enterprises, start-ups and developers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been designed to take more time to consider their actions, resulting in higher accuracy. These designs are especially reliable in science, coding, and thinking 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 successor of the o1 reasoning design. OpenAI also revealed o3-mini, a lighter and much faster variation of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and archmageriseswiki.com security scientists had the opportunity to obtain early access to these models. [214] The model is called o3 instead of o2 to prevent confusion with telecommunications providers 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 model to perform substantial web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to thirty minutes. [216] With browsing and Python tools allowed, 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 design that is trained to examine the semantic resemblance between text and images. It can especially be used for oeclub.org 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 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 produce corresponding images. It can develop pictures of reasonable things ("a stained-glass window with an image of a blue strawberry") along with 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 variation of the model with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new primary system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

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

Sora

Sora is a text-to-video model that can generate videos based on brief detailed prompts [223] as well as extend existing videos forwards or setiathome.berkeley.edu in reverse in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.

Sora's advancement team named it after the Japanese word for "sky", to symbolize its "endless creative capacity". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that function, but did not reveal 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 up to one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the design's capabilities. [225] It acknowledged a few of its imperfections, including battles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but kept in mind that they need to have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have revealed considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's ability to generate reasonable video from text descriptions, mentioning its possible to transform storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause prepare 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 diverse audio and is also a multi-task model that can perform multilingual speech recognition in addition to speech translation and language recognition. [229]
Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can produce tunes with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall under chaos the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for the internet mental 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 category, artist, and a snippet of lyrics and outputs song samples. OpenAI specified the tunes "reveal 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 significant gap" between Jukebox and human-generated music. The Verge stated "It's technically excellent, even if the results seem like mushy variations of tunes that may feel familiar", while Business Insider stated "remarkably, some of the resulting songs are catchy and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches devices to debate toy issues in front of a human judge. The purpose is to research whether such an approach might 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 substantial layer and nerve cell of eight neural network models which are frequently studied in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different 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 provides a conversational user interface that permits users to ask concerns in natural language. The system then reacts with an answer within seconds.