GitLab

Announced in 2016, Gym is an open-source Python library created to assist in the development of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research, making released research more quickly reproducible [24] [144] while supplying users with an easy user interface for interacting 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 learning (RL) research study on video games [147] utilizing RL algorithms and research . Prior RL research focused mainly on enhancing representatives to resolve single jobs. Gym Retro offers the ability to generalize in between video games with similar principles however various appearances.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack knowledge of how to even stroll, however are provided the goals of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the representatives learn how to adapt to changing conditions. When an agent is then gotten rid of from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had learned how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competitors in between agents might create an intelligence "arms race" that could increase a representative's capability to operate even outside the context of the competition. [148]
OpenAI 5

OpenAI Five is a team of 5 OpenAI-curated bots used in the competitive five-on-five computer game Dota 2, that discover to play against human players at a high ability 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 premiere championship competition for the game, where Dendi, a professional Ukrainian player, lost against a bot in a live one-on-one match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually found out by playing against itself for 2 weeks of actual time, and that the learning software was a step in the direction of producing software that can deal with complex tasks like a surgeon. [152] [153] The system uses a type of reinforcement knowing, as the bots learn with time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an opponent and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a full team of 5, and they were able to defeat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert gamers, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the reigning world champs 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 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 player shows the challenges of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown making use of deep support learning (DRL) agents to attain superhuman proficiency in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robot hand, to manipulate physical items. [167] It finds out completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the object orientation problem by using domain randomization, a simulation approach which exposes the student to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having motion tracking video cameras, likewise has RGB electronic cameras to permit the robotic to control an approximate item 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 fix a Rubik's Cube. The robot had the ability 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 enhancing the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually harder environments. ADR varies from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

In June 2020, OpenAI revealed a multi-purpose API which it said was "for accessing new AI designs developed by OpenAI" to let designers get in touch with it for "any English language AI job". [170] [171]
Text generation

The company has actually promoted 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 written by Alec Radford and his colleagues, and released in preprint on OpenAI's site on June 11, 2018. [173] It showed how a generative model of language could obtain world understanding and process long-range dependences by pre-training on a varied corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only limited demonstrative variations at first launched to the public. The complete version of GPT-2 was not instantly launched due to issue about prospective abuse, consisting of applications for composing phony news. [174] Some experts expressed uncertainty that GPT-2 presented a significant danger.

In action 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 innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would hush 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 websites host interactive demonstrations of various circumstances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue without supervision language designs to be general-purpose students, illustrated by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more 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 avoids 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 a not being watched transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete variation of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million specifications were also trained). [186]
OpenAI specified that GPT-3 succeeded at certain "meta-learning" tasks and might 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 drastically improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or encountering the basic capability constraints of predictive language designs. [187] Pre-training GPT-3 required several thousand wiki.vst.hs-furtwangen.de petaflop/s-days [b] of calculate, compared to tens of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not instantly released to the public for concerns of possible abuse, although OpenAI prepared to enable 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 certified 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 is the AI powering the code autocompletion tool GitHub Copilot. [193] In August 2021, an API was launched in private beta. [194] According to OpenAI, the model can develop working code in over a dozen programming languages, most successfully in Python. [192]
Several concerns with glitches, style flaws 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 announced that they would terminate 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), efficient in accepting text or image inputs. [199] They announced that the upgraded technology passed a simulated law school bar examination with a rating around the top 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 produce as much as 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained a few of the issues with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to expose numerous technical details and statistics about GPT-4, such as the precise size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and generate 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 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 sized variation of GPT-4o replacing 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 useful for enterprises, start-ups and designers looking for to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have actually been developed to take more time to consider their responses, causing higher accuracy. These models are particularly reliable in science, coding, and reasoning tasks, 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 revealed o3, the successor of the o1 thinking design. OpenAI likewise revealed o3-mini, a lighter and much faster version of OpenAI o3. Since December 21, 2024, this model 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 scientists had the chance to obtain early access to these models. [214] The design is called o3 rather than o2 to prevent confusion with telecoms companies O2. [215]
Deep research

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design to carry out extensive web surfing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) benchmark. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic resemblance in between text and images. It can notably be used for image category. [217]
Text-to-image

DALL-E

Revealed in 2021, DALL-E is a Transformer model that creates images from textual descriptions. [218] DALL-E uses 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 create corresponding images. It can develop pictures of realistic objects ("a stained-glass window with an image of a blue strawberry") in addition to objects 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 announced DALL-E 2, an updated version of the design with more sensible results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new basic 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 design better able to produce images from intricate descriptions without manual timely 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 on brief detailed prompts [223] along with extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora's advancement team named it after the Japanese word for "sky", to signify its "unlimited imaginative capacity". [223] Sora's technology is an adaptation of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system using publicly-available videos in addition to copyrighted videos certified for that purpose, 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, specifying that it could create videos as much as one minute long. It likewise shared a technical report highlighting the approaches used to train the model, and the design's abilities. [225] It acknowledged some of its drawbacks, including battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "excellent", but kept in mind that they must have been cherry-picked and may not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have revealed substantial interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's ability to create sensible video from text descriptions, mentioning its potential to transform storytelling and content production. 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 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 anticipate subsequent musical notes in MIDI music files. It can create songs with 10 instruments in 15 styles. According to The Verge, a song produced by MuseNet tends to begin fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to develop 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 mentioned the tunes "reveal local musical coherence [and] follow conventional chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" and that "there is a substantial gap" between Jukebox and human-generated music. The Verge mentioned "It's technologically remarkable, even if the outcomes seem like mushy versions of songs that may feel familiar", while Business Insider stated "surprisingly, a few of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
User interfaces

Debate Game

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

Released in 2020, Microscope [239] is a collection of visualizations of every significant layer and nerve cell of 8 neural network models which are often studied in interpretability. [240] Microscope was created to examine the features that form inside these neural networks easily. The models included are AlexNet, VGG-19, different variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is an artificial intelligence tool constructed on top of GPT-3 that offers a conversational user interface that permits users to ask concerns in natural language. The system then reacts with an answer within seconds.