Announced in 2016, Gym is an open-source Python library developed to help with the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research study, disgaeawiki.info making released research more quickly reproducible [24] [144] while supplying users with a simple user interface for interacting with these environments. In 2022, new advancements of Gym have been moved to the library Gymnasium. [145] [146]

Gym Retro

Released in 2018, Gym Retro is a platform for support learning (RL) research on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing agents to resolve single jobs. Gym Retro gives the capability to generalize in between video games with similar concepts however different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot agents at first lack knowledge of how to even stroll, but are provided the objectives of learning to move and to press the opposing representative out of the ring. [148] Through this adversarial learning procedure, the agents learn how to adapt to altering conditions. When a representative is then gotten rid of from this virtual environment and placed in a new virtual environment with high winds, the representative braces to remain upright, recommending it had found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents might create an intelligence "arms race" that might increase an agent's ability to work even outside the context of the competition. [148]

OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that learn to play against human players at a high ability level completely through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation occurred at The International 2017, the annual best champion competition for the video game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for 2 weeks of actual time, and that the learning software application was a step in the instructions of producing software application that can handle complex tasks like a surgeon. [152] [153] The system utilizes 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 capability of the bots expanded to play together as a full group of 5, and they had the ability 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 players, but wound 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, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later on that month, where they played in 42,729 overall games in a four-day open online competition, winning 99.4% of those games. [165]

OpenAI 5's systems in Dota 2's bot gamer shows the difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has actually demonstrated making use of deep support knowing (DRL) agents to attain superhuman skills in Dota 2 matches. [166]

Dactyl

Developed in 2018, Dactyl uses device finding out to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It learns completely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the object orientation problem by utilizing domain randomization, a simulation technique which exposes the learner to a range of experiences rather than trying to fit to truth. The set-up for Dactyl, aside from having motion tracking electronic cameras, likewise has RGB cameras to permit the robot to manipulate an arbitrary object 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 might resolve a Rubik's Cube. The robotic was able to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complex physics that is harder to model. OpenAI did this by improving the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique 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 models established by OpenAI" to let developers call on it for "any English language AI job". [170] [171]

Text generation

The company has popularized generative pretrained transformers (GPT). [172]

OpenAI's original GPT design ("GPT-1")

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could obtain world understanding and process long-range dependencies by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is a not being watched transformer language design and the follower to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with just restricted demonstrative variations initially launched to the public. The complete version of GPT-2 was not right away released due to issue about potential abuse, including applications for composing phony news. [174] Some experts expressed uncertainty that GPT-2 presented a substantial threat.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology 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 variation of the GPT-2 language design. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]

GPT-2's authors argue unsupervised language designs to be general-purpose learners, illustrated 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain concerns encoding vocabulary with word tokens by utilizing 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 a without supervision transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the full variation of GPT-3 contained 175 billion parameters, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the complete variation of GPT-2 (although GPT-3 designs with as couple of as 125 million specifications were also trained). [186]

OpenAI stated that GPT-3 succeeded 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 higgledy-piggledy.xyz German. [184]

GPT-3 significantly improved benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or experiencing the basic ability constraints of predictive language models. [187] Pre-training GPT-3 required several 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 issues of possible abuse, although OpenAI planned to permit 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 exclusively 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 released in personal beta. [194] According to OpenAI, the design can create working code in over a lots programs languages, a lot of efficiently in Python. [192]

Several issues with problems, style flaws and security vulnerabilities were cited. [195] [196]

GitHub Copilot has been implicated of giving off copyrighted code, without any author attribution or license. [197]

OpenAI announced that they would cease 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), efficient in 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 could also read, examine or create as much as 25,000 words of text, and compose code in all significant programming 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 some of the problems with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal various technical details and stats about GPT-4, such as the precise size of the model. [203]

GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained advanced lead to voice, multilingual, and vision criteria, 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 launched GPT-4o mini, a smaller variation of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT user interface. Its API costs $0.15 per million input tokens and wiki.rolandradio.net $0.60 per million output tokens, compared to $5 and $15 respectively for GPT-4o. OpenAI expects it to be particularly useful for enterprises, garagesale.es startups and developers seeking to automate services with AI agents. [208]

o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been created to take more time to consider their responses, resulting in greater precision. These models are particularly reliable in science, coding, and thinking jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was changed by o1. [211]

o3

On December 20, 2024, OpenAI unveiled o3, the successor of the o1 reasoning model. OpenAI also 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 testing o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid confusion with telecommunications services service provider O2. [215]

Deep research

Deep research study is an agent developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to carry out substantial web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]

Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic resemblance between text and images. It can especially be used for image classification. [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 version of GPT-3 to analyze natural language inputs (such as "a green leather handbag shaped like a pentagon" or "an isometric view of a sad capybara") and produce matching images. It can create images of sensible items ("a stained-glass window with a picture of a blue strawberry") in addition to 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 updated variation of the model with more realistic outcomes. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new simple 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 powerful design better able to produce 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 design that can generate videos based on short detailed prompts [223] along with extend existing videos forwards or in reverse 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 represent its "endless imaginative capacity". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos certified for forum.altaycoins.com that function, but did not expose the number or the precise sources of the videos. [223]

OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, stating that it might create videos up to one minute long. It also shared a technical report highlighting the techniques used to train the model, and the design's abilities. [225] It acknowledged some of its shortcomings, consisting of battles simulating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however noted that they need to have been cherry-picked and may not represent Sora's common output. [225]

Despite uncertainty from some academic leaders following Sora's public demo, notable entertainment-industry figures have shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's ability to create practical video from text descriptions, citing its potential to revolutionize storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause prepare for expanding his Atlanta-based film 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 varied audio and is also a multi-task design that can carry out multilingual speech recognition along with 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 create tunes with 10 instruments in 15 designs. According to The Verge, a tune produced by MuseNet tends to begin fairly but 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 mental thriller Ben Drowned to develop 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 snippet of lyrics and outputs song samples. OpenAI specified the songs "show local musical coherence [and] follow standard chord patterns" however acknowledged that the songs lack "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 highly outstanding, even if the outcomes sound like mushy versions of tunes that might feel familiar", while Business Insider specified "remarkably, some of the resulting songs are appealing and sound genuine". [234] [235] [236]

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 whether such an approach may 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 substantial layer and neuron of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was produced to examine the features that form inside these neural networks easily. The designs included are AlexNet, VGG-19, various versions of Inception, and different variations of CLIP Resnet. [241]

ChatGPT

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