Announced in 2016, Gym is an open-source Python library created to facilitate the development of reinforcement learning algorithms. It aimed to standardize how environments are specified in AI research, making published research more quickly reproducible [24] [144] while supplying users with an easy user interface for interacting 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 knowing (RL) research on computer game [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on enhancing representatives to resolve single tasks. Gym Retro gives the capability to generalize in between video games with similar principles but different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first do not have knowledge of how to even walk, however are provided the goals of discovering to move and to push the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents discover how to adjust to altering conditions. When a representative is then removed from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors in between representatives might produce an intelligence "arms race" that could increase a representative's capability to work even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of five 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 entirely through experimental algorithms. Before becoming a team of 5, the very first public demonstration occurred at The International 2017, the yearly best champion competition for the game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had found out by playing against itself for two weeks of real time, and that the knowing software was an action in the direction of developing software that can deal with complex tasks like a surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots discover gradually by playing against themselves numerous 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 broadened to play together as a complete team of 5, and they were able to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat 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' last public appearance came later that month, where they played in 42,729 total video games in a four-day open online competition, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown the use of deep reinforcement learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses device discovering to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It learns 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 method which exposes the learner to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having motion tracking electronic cameras, also has RGB cameras to enable the robotic to manipulate an approximate things by seeing it. In 2018, OpenAI revealed that the system had the ability to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might resolve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube present 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 creating progressively more difficult environments. ADR varies from manual domain randomization by not requiring a human to define randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new AI models established by OpenAI" to let designers call on it for "any English language AI task". [170] [171]
Text generation

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

The original paper on generative pre-training of a transformer-based language model was written by Alec Radford and his coworkers, and released in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world knowledge and process long-range dependencies by pre-training on a varied corpus with long stretches of contiguous 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 model ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative variations at first released to the public. The complete variation of GPT-2 was not right away launched due to concern about potential misuse, including applications for writing phony news. [174] Some specialists revealed uncertainty that GPT-2 posed a considerable danger.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural fake news". [175] Other researchers, such as Jeremy Howard, warned of "the technology to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language model. [177] Several websites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose learners, shown by GPT-2 attaining state-of-the-art 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 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 utilizing 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 an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated that the complete 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 couple of as 125 million parameters were likewise trained). [186]
OpenAI mentioned that GPT-3 was successful at certain "meta-learning" tasks and could generalize the function of a single input-output pair. The GPT-3 release paper provided examples of translation and cross-linguistic transfer learning in between English and Romanian, and between English and German. [184]
GPT-3 dramatically improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language models might be approaching or experiencing the basic ability 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 complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the general public for raovatonline.org issues of possible abuse, although OpenAI planned to permit gain access to through a paid cloud API after a two-month totally free personal beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified specifically to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually 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 launched in personal beta. [194] According to OpenAI, the design can produce working code in over a lots programming languages, a lot of effectively in Python. [192]
Several problems with problems, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been implicated of discharging 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), capable of accepting text or image inputs. [199] They announced that the updated technology 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 could also check out, evaluate or create approximately 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caution that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and stats 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 create text, images and audio. [204] GPT-4o attained state-of-the-art lead to voice, wiki.snooze-hotelsoftware.de 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) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller 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 beneficial 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 designs, which have actually been designed to take more time to think of their reactions, causing higher precision. These designs are especially reliable in science, coding, and reasoning 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 revealed o3-mini, a lighter and much faster variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and bytes-the-dust.com security scientists had the chance to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecommunications companies O2. [215]
Deep research study

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out extensive web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to thirty minutes. [216] With searching and Python tools enabled, it reached a precision 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 model that is trained to examine the semantic similarity in between text and images. It can notably be used for image classification. [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 interpret natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of a sad capybara") and create corresponding images. It can create pictures of realistic objects ("a stained-glass window with an image of a blue strawberry") along with things 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 announced DALL-E 2, an updated variation of the model with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new simple system for transforming a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful model much better able to produce images from intricate 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 model that can create videos based on short detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can produce videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of generated videos is unknown.

Sora's advancement group named it after the Japanese word for "sky", to signify its "unlimited creative capacity". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that purpose, but did not reveal the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it could generate videos up to one minute long. It also shared a technical report highlighting the methods utilized to train the design, and the model's capabilities. [225] It acknowledged some of its imperfections, consisting of struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but kept in mind that they need to have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have shown considerable interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation's capability to create reasonable video from text descriptions, mentioning its possible to revolutionize storytelling and material development. He said that his enjoyment about Sora's possibilities was so strong that he had actually decided to pause prepare for expanding his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a large dataset of diverse audio and is likewise 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, is a deep neural net trained to anticipate subsequent musical notes in MIDI music files. It can generate tunes with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to start fairly however then fall into turmoil the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were used as early as 2020 for the internet 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 category, artist, and a snippet of lyrics and outputs tune samples. OpenAI mentioned the songs "show regional musical coherence [and] follow conventional chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a substantial gap" between Jukebox and human-generated music. The Verge stated "It's highly impressive, even if the results sound like mushy versions of tunes that may feel familiar", while Business Insider stated "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 machines to dispute toy problems in front of a human judge. The function is to research study whether such an approach may assist 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 8 neural network models which are frequently studied in interpretability. [240] Microscope was developed to examine the functions that form inside these neural networks easily. The designs included are AlexNet, VGG-19, different variations of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

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