24cyber

Announced in 2016, Gym is an open-source Python library created to help with the advancement of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research, making released research more easily reproducible [24] [144] while providing users with an easy interface for connecting with these environments. In 2022, brand-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 on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing agents to fix single tasks. Gym Retro offers the capability to generalize in between games with comparable concepts however different appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have knowledge of how to even stroll, but are given the objectives of learning to move and to press the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adjust to changing conditions. When an agent is then removed from this virtual environment and placed 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 method. [148] [149] OpenAI's argued that competition between agents could produce an intelligence "arms race" that might increase a representative's capability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of five OpenAI-curated bots used in the competitive five-on-five video game Dota 2, that discover to play against human gamers at a high ability level totally through experimental algorithms. Before becoming a group of 5, the very first public demonstration occurred at The International 2017, the annual best champion tournament for the video game, where Dendi, an expert 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 discovered by playing against itself for two weeks of actual time, and that the learning software was a step in the direction of developing software that can manage intricate tasks like a surgeon. [152] [153] The system uses a form of support knowing, as the bots discover gradually by playing against themselves hundreds of times a day for months, and 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 team 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 2 exhibition matches against professional players, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champions of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' final public appearance came later on that month, where they played in 42,729 total 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 obstacles of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown the use of deep support knowing (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device learning to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It learns completely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB cams 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 demonstrated that Dactyl could fix 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 effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of generating gradually harder environments. ADR varies from manual domain randomization by not needing a human to define randomization ranges. [169]
API

In June 2020, OpenAI revealed 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 task". [170] [171]
Text generation

The company has 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 design was composed by Alec Radford and his associates, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed 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 an unsupervised transformer language model and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was announced in February 2019, with just minimal demonstrative versions at first released to the general public. The full variation of GPT-2 was not instantly launched due to concern about potential misuse, consisting of applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 presented a significant danger.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to spot "neural phony news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out 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 presentations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose learners, highlighted by GPT-2 attaining modern precision and perplexity on 7 of 8 zero-shot jobs (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 at least 3 upvotes. It prevents certain problems 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 design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full version 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 couple of as 125 million parameters were likewise trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between English and systemcheck-wiki.de Romanian, and in between English and German. [184]
GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or experiencing the essential capability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained model was not immediately launched to the public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month complimentary personal beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed exclusively 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 personal beta. [194] According to OpenAI, the design can develop working code in over a lots programs languages, the majority of successfully in Python. [192]
Several concerns with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of producing copyrighted code, without any author attribution or license. [197]
OpenAI revealed 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 technology passed a simulated law school bar exam 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 might also check out, examine or produce approximately 25,000 words of text, and compose 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 iteration, with the caution that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is also capable of taking images as input on ChatGPT. [202] OpenAI has declined to reveal various technical details and stats 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 produce text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, multilingual, and vision criteria, setting brand-new records in audio speech recognition 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 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, startups and designers 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 think about their reactions, leading to greater precision. These designs are particularly effective in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Staff member. [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 thinking model. OpenAI also 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 chance to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecoms companies O2. [215]
Deep research study

Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to carry out extensive 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 analyze the semantic similarity in 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 develops images from textual descriptions. [218] DALL-E uses 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 matching images. It can develop images of realistic things ("a stained-glass window with a picture of a blue strawberry") along with items 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 version of the model with more realistic results. [219] In December 2022, OpenAI published 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 produce images from complicated descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was released to the general public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

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

Sora's advancement team named it after the Japanese word for "sky", to signify its "limitless creative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos certified for that purpose, but did not expose 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, stating that it could produce videos up to one minute long. It likewise shared a technical report highlighting the methods utilized to train the design, and the model's capabilities. [225] It acknowledged a few of its imperfections, consisting of struggles mimicing intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "remarkable", however kept in mind that they should have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, noteworthy entertainment-industry figures have actually shown considerable 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 prospective to change storytelling and content production. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause prepare for expanding his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment 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 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 songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to start fairly but then fall into mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for the internet mental thriller Ben Drowned to produce 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 genre, artist, and a bit of lyrics and outputs song samples. OpenAI stated the songs "show local musical coherence [and] follow conventional chord patterns" but acknowledged that the songs do not have "familiar bigger musical structures such as choruses that duplicate" and that "there is a considerable space" in between Jukebox and human-generated music. The Verge specified "It's technologically impressive, even if the results seem like mushy variations of songs that might feel familiar", while Business Insider stated "surprisingly, some of the resulting tunes are appealing and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to debate toy issues in front of a human judge. The function is to research study whether such a technique might assist in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every substantial layer and neuron of eight neural network designs which are often studied in interpretability. [240] Microscope was created to evaluate the features that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool developed on top of GPT-3 that provides a conversational interface that enables users to ask concerns in natural language. The system then responds with a response within seconds.