Announced in 2016, Gym is an open-source Python library developed to facilitate the development of support knowing algorithms. It aimed to standardize how environments are specified in AI research study, making published research study more quickly reproducible [24] [144] while providing users with an easy 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 reinforcement learning (RL) research study on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to fix single jobs. Gym Retro provides the capability to generalize between video games with similar principles but various looks.

RoboSumo

Released in 2017, wiki.dulovic.tech RoboSumo is a virtual world where humanoid metalearning robot representatives initially lack understanding of how to even stroll, however are provided the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the agents discover how to adjust to altering conditions. When a representative is then removed from this virtual environment and put in a 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 competition in between agents might produce an intelligence "arms race" that could increase a representative's capability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team 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 totally through trial-and-error algorithms. Before ending up being a group of 5, the very first public demonstration 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 individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for 2 weeks of actual time, which the knowing software application was an action in the direction of creating software that can deal with intricate tasks like a cosmetic surgeon. [152] [153] The system utilizes a kind of support knowing, as the bots learn with time by playing against themselves numerous times a day for months, and archmageriseswiki.com are rewarded for actions such as killing an opponent and taking map objectives. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a complete group of 5, and they were able 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 professional gamers, yewiki.org but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last public look came later that month, where they played in 42,729 overall video games in a four-day open online competition, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the obstacles of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has shown using deep reinforcement knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes machine finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical things. [167] It discovers totally in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB electronic cameras to enable the robot to control an arbitrary 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 showed that Dactyl could resolve a Rubik's Cube. The robot was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to model. OpenAI did this by improving the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of creating gradually more tough 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 designs established by OpenAI" to let developers contact it for "any English language AI job". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language could 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 an unsupervised transformer language model and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative versions initially launched to the general public. The complete variation of GPT-2 was not instantly released due to concern about possible misuse, including applications for composing phony news. [174] Some professionals expressed uncertainty that GPT-2 postured a significant risk.

In reaction 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, alerted of "the innovation to absolutely 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 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 models to be general-purpose learners, highlighted by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not more trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain issues encoding vocabulary with word tokens by using byte pair encoding. This permits 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 model and the successor to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion specifications, [184] two 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 parameters were likewise trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing in 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 basic ability constraints of predictive language designs. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the general public for concerns 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 started 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 actually 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 personal beta. [194] According to OpenAI, the design can produce working code in over a lots shows languages, a lot of effectively in Python. [192]
Several problems with problems, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has been accused of discharging copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would terminate 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 examination 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 might also check out, evaluate or generate approximately 25,000 words of text, and compose code in all major shows languages. [200]
Observers reported that the version 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 problems with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal various technical details and statistics about GPT-4, such as the exact size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision standards, 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 version of GPT-4o replacing GPT-3.5 Turbo on the 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 launched the o1-preview and o1-mini models, which have actually been created to take more time to consider their responses, leading to higher accuracy. These models are particularly efficient in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was replaced by o1. [211]
o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 thinking design. OpenAI likewise unveiled o3-mini, a lighter and much faster version of OpenAI o3. Since December 21, 2024, this design is not available for wavedream.wiki public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security scientists had the chance to obtain early access to these models. [214] The model is called o3 rather than o2 to prevent confusion with telecommunications companies O2. [215]
Deep research study

Deep research is a representative established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform comprehensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools allowed, 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 model that is trained to analyze the semantic resemblance in between text and images. It can notably be used 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 utilizes 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 matching images. It can produce images of sensible items ("a stained-glass window with an image of a blue strawberry") along with items 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 revealed DALL-E 2, an upgraded variation of the model with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-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 design much better able to generate images from complex descriptions without manual timely engineering and render intricate details like hands and text. [221] It was released 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 produce videos based on brief detailed triggers [223] along with extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.

Sora's development team called it after the Japanese word for "sky", to represent its "limitless 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 in addition to copyrighted videos accredited for that purpose, however did not expose the number or the exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, stating that it could produce videos up to one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the model's abilities. [225] It acknowledged a few of its imperfections, consisting of battles simulating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", but noted that they should have been cherry-picked and may not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have revealed significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to generate realistic video from text descriptions, citing its possible to reinvent storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to stop briefly strategies for expanding his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment design. [228] It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech acknowledgment in addition to speech translation and language identification. [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 tune produced by MuseNet tends to begin fairly however 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 web psychological thriller Ben Drowned to create music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to generate 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 stated the songs "reveal regional musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar bigger musical structures such as choruses that repeat" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge mentioned "It's technologically excellent, even if the results sound like mushy versions of tunes that might 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 launched the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The purpose is to research study whether such a method 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 considerable layer and wiki.whenparked.com nerve cell of 8 neural network designs which are often studied in interpretability. [240] Microscope was produced to evaluate the features that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an artificial intelligence tool constructed on top of GPT-3 that provides a conversational interface that allows users to ask concerns in natural language. The system then responds with an answer within seconds.