knightcomputers

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 study more easily reproducible [24] [144] while providing users with a simple user interface for interacting with these environments. In 2022, brand-new advancements of Gym have been transferred to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing representatives to fix single tasks. Gym Retro provides the ability to generalize in between games with comparable principles however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents at first lack understanding of how to even stroll, however are offered the objectives of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial knowing procedure, the agents find out how to adjust to changing conditions. When an agent is then removed from this virtual environment and put in a brand-new virtual environment with high winds, the representative braces to remain upright, recommending it had discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives could develop an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five video game Dota 2, that discover to play against human players at a high skill level entirely through trial-and-error algorithms. Before becoming a team of 5, the first public presentation happened at The International 2017, the yearly premiere championship tournament for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for 2 weeks of real time, which the knowing software application was an action in the instructions of developing software application that can deal with complex jobs like a cosmetic surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots discover with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an enemy 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 groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against professional players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the video game at the time, pipewiki.org 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 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 shows the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown using deep reinforcement learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robotic hand, bytes-the-dust.com to manipulate physical items. [167] It discovers totally in simulation utilizing the very same RL algorithms and higgledy-piggledy.xyz training code as OpenAI Five. OpenAI dealt with the item orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences instead of trying to fit to reality. The set-up for Dactyl, aside from having movement tracking cams, also has RGB cameras to enable the robotic to manipulate an arbitrary item by seeing it. In 2018, OpenAI showed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate 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 method of producing progressively harder environments. ADR differs 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 new AI models developed by OpenAI" to let designers contact 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 model ("GPT-1")

The initial paper on generative pre-training of a transformer-based language design was written by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world understanding and procedure long-range dependences by pre-training on a varied corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language model and the follower to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions at first launched to the public. The complete variation of GPT-2 was not instantly launched due to issue about possible abuse, consisting of applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 presented a significant hazard.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "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 drown out all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the complete variation of the GPT-2 language design. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language models to be general-purpose students, highlighted by GPT-2 attaining cutting edge 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 a little 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 using 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 an unsupervised transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the complete version of GPT-3 contained 175 billion specifications, [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 couple of as 125 million criteria were likewise trained). [186]
OpenAI specified 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 gave examples of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
GPT-3 drastically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or encountering the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 model. [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 planned to enable 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 certified solely 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 released in private beta. [194] According to OpenAI, the model can create working code in over a dozen programs languages, many successfully in Python. [192]
Several issues with problems, design flaws and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been implicated of producing copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would stop 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 announced 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 likewise check out, examine or generate up to 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat 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 decreased to reveal different technical details and yewiki.org data about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, archmageriseswiki.com and vision criteria, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched GPT-4o mini, a smaller sized variation 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 particularly useful for business, start-ups and designers seeking to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have actually been designed to take more time to think about their responses, leading to higher accuracy. These models are especially effective in science, coding, and reasoning tasks, and were made available to ChatGPT Plus and Employee. [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 likewise unveiled o3-mini, a lighter and quicker variation of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications providers O2. [215]
Deep research

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities 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 browsing and Python tools enabled, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic similarity in 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 model that produces images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and generate corresponding images. It can produce images of reasonable objects ("a stained-glass window with an image 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 revealed DALL-E 2, an upgraded variation of the design with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new fundamental 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 effective 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 create videos based on brief detailed prompts [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.

Sora's advancement team named it after the Japanese word for "sky", oeclub.org to signify its "unlimited imaginative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos accredited for that function, however did not expose the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, mentioning that it could generate videos up to one minute long. It likewise shared a technical report highlighting the methods utilized to train the model, and the model's capabilities. [225] It acknowledged some of its drawbacks, consisting of struggles imitating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "remarkable", however noted that they must have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have revealed considerable interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's capability to generate practical video from text descriptions, mentioning its possible to reinvent storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had chosen to stop briefly prepare for expanding 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 large dataset of diverse audio and is also a multi-task design that can perform multilingual speech acknowledgment as well as 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 songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to begin fairly but then fall into turmoil the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to produce 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 bit of lyrics and outputs tune samples. OpenAI stated the tunes "reveal local musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that duplicate" which "there is a considerable space" in between Jukebox and human-generated music. The Verge stated "It's technologically excellent, even if the outcomes sound like mushy variations of songs that may feel familiar", while Business Insider specified "remarkably, some of the resulting songs are catchy and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches makers to dispute toy issues in front of a human judge. The purpose is to research study whether such a technique might help in auditing AI choices and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of eight neural network designs which are often in interpretability. [240] Microscope was created to evaluate the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various variations of Inception, and various 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 an answer within seconds.