Announced in 2016, Gym is an open-source Python library designed to help with the advancement of support learning algorithms. It aimed to standardize how environments are defined in AI research, making released research more quickly reproducible [24] [144] while offering users with an easy user interface for engaging with these environments. In 2022, new advancements of Gym have been transferred to the library Gymnasium. [145] [146]

Gym Retro

Released in 2018, Gym Retro is a platform for support knowing (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to solve single jobs. Gym Retro gives the ability to generalize between video games with similar concepts however various appearances.

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 given the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial learning process, the agents learn how to adjust to altering conditions. When a representative is then eliminated from this virtual environment and placed in a brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually learned how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between agents could 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 five OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human gamers at a high skill level completely through trial-and-error algorithms. Before ending up being a group of 5, the first public demonstration occurred at The International 2017, the annual best championship competition for the 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 two weeks of genuine time, which the learning software was an action in the direction of developing software application that can manage intricate jobs like a surgeon. [152] [153] The system uses a kind of reinforcement knowing, as the bots learn in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]

By June 2018, the capability of the bots broadened to play together as a complete group of 5, and they were able to defeat groups of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert gamers, however wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the game at the time, 2:0 in a live exhibition match in San Francisco. [163] [164] The bots' last 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 difficulties of AI systems in multiplayer online battle arena (MOBA) games and how OpenAI Five has shown using deep reinforcement learning (DRL) agents to attain superhuman competence in Dota 2 matches. [166]

Dactyl

Developed in 2018, Dactyl utilizes maker learning to train a Shadow Hand, a human-like robot hand, to control physical items. [167] It learns entirely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by utilizing domain randomization, a simulation approach which exposes the learner to a variety of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, also has RGB cams to enable the robotic to control an approximate things by seeing it. In 2018, OpenAI revealed that the system had the ability to control a cube and an octagonal prism. [168]

In 2019, OpenAI demonstrated that Dactyl could resolve a Rubik's Cube. The robotic was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to model. OpenAI did this by enhancing the effectiveness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation approach of producing progressively more challenging environments. ADR differs from manual domain randomization by not needing a human to specify randomization ranges. [169]

API

In June 2020, OpenAI announced a multi-purpose API which it said was "for accessing brand-new AI models developed by OpenAI" to let developers get in touch with it for "any English language AI task". [170] [171]

Text generation

The business has 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 design was composed by Alec Radford and his associates, and released in preprint on OpenAI's website on June 11, 2018. [173] It showed how a generative design of language might obtain world knowledge 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 successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was revealed in February 2019, with only restricted demonstrative versions at first released to the general public. The full version of GPT-2 was not instantly released due to concern about potential misuse, consisting of applications for writing fake news. [174] Some experts revealed uncertainty that GPT-2 postured a substantial threat.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to discover "neural fake news". [175] Other scientists, such as Jeremy Howard, alerted of "the technology to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush all other speech and be impossible to filter". [176] In November 2019, OpenAI launched the total version of the GPT-2 language model. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer models. [178] [179] [180]

GPT-2's authors argue unsupervised language models to be general-purpose students, highlighted by GPT-2 attaining cutting edge accuracy 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 a little 40 gigabytes of text from URLs shared in Reddit submissions with at least 3 upvotes. It avoids certain problems 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 a not being watched transformer language design and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude larger than the 1.5 billion [185] in the full 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 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 between English and German. [184]

GPT-3 dramatically improved benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required several thousand petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the complete GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained design was not right away released to the public for issues of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month complimentary private beta that started 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 released in private beta. [194] According to OpenAI, the design can produce working code in over a lots shows languages, a lot of efficiently in Python. [192]

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

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

OpenAI revealed that they would stop 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 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 read, evaluate or produce up to 25,000 words of text, and compose code in all major programs languages. [200]

Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based iteration, gratisafhalen.be with the caution that GPT-4 retained a few of the issues 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 model. [203]

GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and generate text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision benchmarks, setting new records in audio speech recognition 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 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 enterprises, start-ups and developers looking for to automate services with AI agents. [208]

o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini designs, which have actually been developed to take more time to consider their reactions, resulting in higher accuracy. These designs are especially efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Team members. [209] [210] In December 2024, o1-preview was replaced by o1. [211]

o3

On December 20, 2024, OpenAI unveiled o3, the follower of the o1 reasoning model. OpenAI likewise unveiled o3-mini, a lighter and quicker variation of OpenAI o3. Since December 21, 2024, this design is not available for public usage. According to OpenAI, they are testing o3 and o3-mini. [212] [213] Until January 10, 2025, gratisafhalen.be security and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]

Deep research study

Deep research is a representative established by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 model to perform extensive web browsing, data 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 classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to evaluate the semantic similarity 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 creates 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 an unfortunate capybara") and produce corresponding images. It can develop images of practical objects ("a stained-glass window with an image of a blue strawberry") in addition to items that do not exist in truth ("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 practical results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a brand-new primary 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 powerful design much better able to produce images from complex descriptions without manual prompt engineering and render intricate details like hands and text. [221] It was launched to the 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 short detailed triggers [223] as well as extend existing videos forwards or in reverse in time. [224] It can create videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.

Sora's advancement group called it after the Japanese word for "sky", to represent its "endless innovative potential". [223] Sora's technology is an adaptation of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos certified for that function, but did not reveal 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, mentioning that it could produce videos up to one minute long. It likewise shared a technical report highlighting the methods used to train the model, and the model's capabilities. [225] It acknowledged some of its drawbacks, including struggles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but noted that they should have been cherry-picked and may not represent Sora's typical output. [225]

Despite uncertainty from some scholastic leaders following Sora's public demo, notable entertainment-industry figures have actually revealed substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's ability to generate sensible video from text descriptions, citing its possible to reinvent storytelling and material creation. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly prepare for broadening 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 carry out multilingual speech recognition as well as speech translation and language identification. [229]

Music generation

MuseNet

Released in 2019, MuseNet is a deep neural net trained to predict 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 start fairly however then fall into mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were used as early as 2020 for surgiteams.com the web mental thriller Ben Drowned to create music for wiki.snooze-hotelsoftware.de 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 tune samples. OpenAI stated the tunes "reveal local musical coherence [and] follow standard chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that duplicate" which "there is a considerable gap" between Jukebox and human-generated music. The Verge specified "It's highly remarkable, even if the results seem like mushy variations of songs that may feel familiar", while Business Insider specified "remarkably, a few of the resulting tunes are appealing and sound legitimate". [234] [235] [236]

Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches devices to discuss toy issues in front of a human judge. The purpose is to research study whether such an approach might assist in auditing AI choices 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 produced to evaluate the functions that form inside these neural networks quickly. The designs included are AlexNet, VGG-19, various variations of Inception, and wiki.myamens.com different variations of CLIP Resnet. [241]

ChatGPT

Launched in November 2022, ChatGPT is an artificial intelligence tool built on top of GPT-3 that provides a conversational user interface that allows users to ask questions in natural language. The system then reacts with a response within seconds.