Announced in 2016, Gym is an open-source Python library designed to help with the development of support knowing algorithms. It aimed to standardize how environments are specified in AI research, making released research study more easily reproducible [24] [144] while supplying users with an easy interface for engaging with these environments. In 2022, new advancements of Gym have been relocated to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] utilizing RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to resolve single tasks. Gym Retro provides the capability to generalize in between video games with similar ideas but 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 stroll, but are offered the objectives of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing 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 brand-new virtual environment with high winds, the agent braces to remain upright, recommending it had actually discovered how to balance in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between representatives could develop an intelligence "arms race" that could increase a representative's ability to operate even outside the context of the competition. [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 discover to play against human gamers at a high ability level totally through experimental algorithms. Before ending up being a group of 5, the very first public presentation occurred at The International 2017, the yearly premiere champion tournament for the video game, where Dendi, an expert 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 learned by playing against itself for two weeks of real time, and that the knowing software application was a step in the instructions of producing software that can manage complex jobs like a surgeon. [152] [153] The system uses a form of support learning, as the bots discover in time by playing against themselves hundreds of times a day for months, and are rewarded for actions such as killing an enemy and taking map goals. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a full team of 5, and they had the ability to defeat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against expert players, but ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' last public look came later 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 gamer 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 reinforcement learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

in 2018, Dactyl uses machine discovering to train a Shadow Hand, a human-like robotic hand, to control physical items. [167] It discovers completely in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI dealt with the item orientation issue by utilizing domain randomization, a simulation method which exposes the learner to a range of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking video cameras, also has RGB cams to enable the robot to manipulate an arbitrary object by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could fix a Rubik's Cube. The robotic had the ability to fix the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation approach of producing gradually harder environments. ADR differs from manual domain randomization by not needing a human to define randomization varieties. [169]
API

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

The business has actually 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 colleagues, and released 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 dependences 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 design and the successor to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative variations at first released to the general public. The full version of GPT-2 was not immediately launched due to concern about potential abuse, consisting of applications for writing fake news. [174] Some professionals revealed uncertainty that GPT-2 positioned a significant risk.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect "neural phony 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 muffle all other speech and be difficult to filter". [176] In November 2019, OpenAI released the total variation of the GPT-2 language model. [177] Several sites host interactive presentations of different instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language designs to be general-purpose students, highlighted by GPT-2 attaining modern precision and wiki.asexuality.org 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 slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It prevents certain problems 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 design and the successor to GPT-2. [182] [183] [184] OpenAI specified that the complete 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 models with as couple of as 125 million specifications were also trained). [186]
OpenAI stated that GPT-3 prospered 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 in between English and Romanian, and between English and German. [184]
GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language models might be approaching or coming across the essential ability constraints of predictive language designs. [187] Pre-training GPT-3 required a number of 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 immediately launched to the public for concerns of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month free private beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified exclusively to Microsoft. [190] [191]
Codex

Announced in mid-2021, Codex is a descendant of GPT-3 that has actually 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 released in private beta. [194] According to OpenAI, the model can create working code in over a dozen programming languages, the majority of successfully in Python. [192]
Several issues with problems, style defects and security vulnerabilities were mentioned. [195] [196]
GitHub Copilot has actually been implicated of discharging copyrighted code, without any 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, wiki-tb-service.com 2023, OpenAI announced the release of Generative Pre-trained Transformer 4 (GPT-4), capable of accepting text or image inputs. [199] They announced that the upgraded innovation passed a simulated law school bar test 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 produce approximately 25,000 words of text, and compose code in all major programming languages. [200]
Observers reported that the iteration of ChatGPT using GPT-4 was an improvement on the previous GPT-3.5-based version, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is likewise efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to expose various technical details and stats about GPT-4, such as the accurate 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 results in voice, multilingual, and vision standards, 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, wiki.whenparked.com OpenAI launched 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 engel-und-waisen.de GPT-4o. OpenAI expects it to be especially beneficial for business, startups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI launched the o1-preview and o1-mini models, which have been created to take more time to think of their reactions, leading to greater accuracy. These designs are especially efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Staff member. [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 thinking model. OpenAI also unveiled o3-mini, a lighter and quicker variation of OpenAI o3. As of December 21, 2024, this design is not available for public usage. According to OpenAI, they are evaluating o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the opportunity to obtain early access to these designs. [214] The model is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215]
Deep research study

Deep research study is a representative established by OpenAI, unveiled 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 thirty minutes. [216] With searching and Python tools made it possible for, it reached a precision 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 model that is trained to evaluate the semantic resemblance 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 produces 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 shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can create images of practical objects ("a stained-glass window with an image of a blue strawberry") as well as 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 upgraded version of the design with more sensible outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a new rudimentary 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 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 launched 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] as well as extend existing videos forwards or backwards in time. [224] It can generate videos with resolution approximately 1920x1080 or 1080x1920. The maximal length of generated videos is unknown.

Sora's development team called it after the Japanese word for "sky", to signify its "limitless creative capacity". [223] Sora's innovation is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos as well as copyrighted videos licensed for that purpose, but did not expose the number or the specific sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could generate 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 a few of its drawbacks, consisting of struggles replicating complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "impressive", however kept in mind that they must have been cherry-picked and might not represent Sora's common output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, noteworthy entertainment-industry figures have shown substantial interest in the innovation's potential. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the technology's capability to generate reasonable video from text descriptions, citing its possible to revolutionize storytelling and content development. He said that his excitement about Sora's possibilities was so strong that he had chosen to stop briefly strategies for garagesale.es expanding his Atlanta-based movie studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of diverse audio and is likewise a multi-task design that can perform multilingual speech recognition in addition to 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 create songs with 10 instruments in 15 designs. According to The Verge, a tune generated by MuseNet tends to start fairly however then fall into chaos the longer it plays. [230] [231] In popular culture, preliminary applications of this tool were used as early as 2020 for the internet psychological 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 category, artist, yewiki.org and engel-und-waisen.de a snippet of lyrics and outputs song samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow traditional chord patterns" however acknowledged that the songs do not have "familiar bigger musical structures such as choruses that repeat" which "there is a significant space" between Jukebox and human-generated music. The Verge stated "It's highly impressive, even if the outcomes sound like mushy versions of songs that may feel familiar", while Business Insider specified "surprisingly, some of the resulting tunes are appealing and sound genuine". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches devices to debate toy problems in front of a human judge. The purpose is to research whether such an approach might assist in auditing AI choices and in establishing 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 frequently studied in interpretability. [240] Microscope was produced to examine the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various variations of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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