furryarabic

Announced in 2016, Gym is an open-source Python library created to help with the development of support learning algorithms. It aimed to standardize how environments are specified in AI research study, making released research more easily reproducible [24] [144] while supplying users with a basic user interface for engaging with these environments. In 2022, new developments of Gym have been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for support learning (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing agents to solve single jobs. Gym Retro provides the capability to generalize between video games with comparable principles but different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents initially lack knowledge of how to even stroll, but are offered the objectives of finding out to move and to press the opposing agent out of the ring. [148] Through this adversarial learning process, the agents find out how to adjust to changing conditions. When an agent is then eliminated from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, suggesting it had actually learned how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives could produce an intelligence "arms race" that could increase an agent's capability to work 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 find out to play against human gamers at a high ability level totally through trial-and-error algorithms. Before ending up being a team of 5, the very first public presentation took place at The International 2017, the annual best championship tournament for the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had learned by playing against itself for two weeks of genuine time, which the learning software was a step in the instructions of producing software application that can deal with intricate tasks like a cosmetic surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots learn gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots broadened to play together as a complete group of 5, and they had the ability 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 video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champions of the video game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public look came later that month, where they played in 42,729 overall games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5's systems in Dota 2's bot player reveals the obstacles of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown using deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It discovers totally in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by using domain randomization, a simulation method which exposes the learner to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking cameras, likewise has RGB cams to permit the robot to control an arbitrary object 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 might solve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce 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 progressively more challenging environments. ADR differs from manual domain randomization by not requiring 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 get in touch with it for "any English language AI job". [170] [171]
Text generation

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

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world knowledge and procedure long-range reliances by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a without supervision transformer language model and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative versions initially released to the public. The complete version of GPT-2 was not instantly released due to issue about prospective misuse, including applications for writing phony news. [174] Some experts expressed uncertainty that GPT-2 posed a substantial risk.

In action to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony 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 muffle all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total variation of the GPT-2 language design. [177] Several sites host interactive presentations of different 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, shown by GPT-2 attaining advanced 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 prevents certain issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits 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 an unsupervised transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned 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 version of GPT-2 (although GPT-3 models with as few as 125 million criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" jobs and might generalize the purpose of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning between English and Romanian, and between English and German. [184]
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or experiencing the fundamental 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 complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not instantly released to the public for concerns of possible abuse, although OpenAI planned to allow 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 additionally been trained on code from 54 million GitHub repositories, [192] [193] and wiki.snooze-hotelsoftware.de 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 model can create working code in over a dozen programming languages, a lot of successfully in Python. [192]
Several problems with glitches, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been implicated of giving off copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would cease support 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), efficient in accepting text or image inputs. [199] They revealed that the updated technology passed a simulated law school bar test with a rating around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could likewise check out, examine or create approximately 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is also efficient in taking images as input on . [202] OpenAI has actually declined to reveal numerous technical details and data about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained state-of-the-art results 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) standard compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized 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 helpful for business, 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 designs, which have been created to take more time to consider their responses, resulting in higher precision. These designs are especially efficient 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 revealed o3, the follower of the o1 thinking design. OpenAI likewise revealed o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this design is not available for public use. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the chance to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecoms services service provider 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 design to carry out comprehensive web surfing, information analysis, and synthesis, delivering 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 design that is trained to examine the semantic similarity between text and images. It can significantly be utilized for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer design that produces 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 handbag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce pictures of realistic things ("a stained-glass window with a picture of a blue strawberry") as well as 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 version of the design with more reasonable results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new primary system for converting 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 complicated descriptions without manual timely engineering and render complex 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 on short detailed prompts [223] as well as extend existing videos forwards or backwards in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The optimum length of created videos is unknown.

Sora's advancement team called it after the Japanese word for "sky", to symbolize its "limitless innovative potential". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos certified for that purpose, however did not reveal the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it could produce videos approximately one minute long. It also shared a technical report highlighting the methods utilized to train the model, and the design's capabilities. [225] It acknowledged a few of its imperfections, including battles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however noted that they need to have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demo, noteworthy entertainment-industry figures have actually shown considerable interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to create practical video from text descriptions, citing its prospective to revolutionize storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had 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 recognition design. [228] It is trained on a big dataset of diverse audio and is also a multi-task design that can carry out multilingual speech recognition along with speech translation and language identification. [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 songs with 10 instruments in 15 styles. According to The Verge, a tune created by MuseNet tends to start fairly but 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 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 genre, artist, and a bit of lyrics and outputs song samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar bigger musical structures such as choruses that repeat" which "there is a significant space" in between Jukebox and human-generated music. The Verge mentioned "It's technologically outstanding, even if the outcomes seem like mushy variations of songs that might feel familiar", while Business Insider stated "remarkably, a few of the resulting songs are appealing and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches devices to debate toy issues in front of a human judge. The function is to research whether such a method might help in auditing AI decisions and in developing 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 models which are often studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks quickly. The models consisted of 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 built on top of GPT-3 that supplies a conversational interface that enables users to ask questions in natural language. The system then responds with a response within seconds.