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

Released in 2018, engel-und-waisen.de Gym Retro is a platform for wiki.dulovic.tech reinforcement learning (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing representatives to fix single tasks. Gym Retro offers the capability to generalize in between games with similar ideas but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives initially do not have understanding of how to even stroll, but are offered the goals of discovering to move and to press the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the representatives learn how to adapt to changing conditions. When a representative is then eliminated from this virtual environment and positioned in a brand-new virtual environment with high winds, the agent braces to remain upright, suggesting it had actually found out 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 might increase a representative’s ability to function even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a group of 5 OpenAI-curated bots utilized in the competitive five-on-five computer game Dota 2, that find out to play against human players at a high ability level entirely through experimental algorithms. Before ending up being a team of 5, the very first public presentation took place at The International 2017, the annual best 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 actual time, and that the learning software was an action in the instructions of developing software application that can manage complex tasks like a surgeon. [152] [153] The system utilizes a type of support learning, as the bots learn over time by playing against themselves numerous 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 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 professional players, however wound 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 on 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 systems in Dota 2’s bot player reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown the usage of deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device learning to train a Shadow Hand, a human-like robotic hand, to manipulate physical items. [167] It learns entirely in simulation using the very same RL algorithms and training code as OpenAI Five. OpenAI dealt with the things orientation issue by utilizing domain randomization, a simulation technique which exposes the student to a range of experiences rather than attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking cams, also has RGB cams to allow the robotic to manipulate an approximate object by seeing it. In 2018, OpenAI showed that the system was able 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 solve the puzzle 60% of the time. Objects like the Rubik’s Cube present complex physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating progressively 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 developers contact it for “any English language AI job”. [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 design was composed by Alec Radford and his coworkers, and released in preprint on OpenAI’s website on June 11, 2018. [173] It demonstrated how a generative design of language could obtain world understanding and process long-range dependences by pre-training on a varied corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 (“GPT-2”) is a without supervision transformer language model and the follower to OpenAI’s original GPT design (“GPT-1”). GPT-2 was announced in February 2019, with just limited demonstrative variations initially released to the public. The full version of GPT-2 was not instantly released due to concern about potential misuse, including applications for writing fake news. [174] Some specialists expressed uncertainty that GPT-2 presented a substantial hazard.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to detect “neural fake news”. [175] Other scientists, such as Jeremy Howard, cautioned of “the innovation to totally fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would muffle all other speech and be impossible to filter”. [176] In November 2019, OpenAI released the total version of the GPT-2 language model. [177] Several websites host interactive presentations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2’s authors argue without supervision language designs to be general-purpose students, illustrated by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not more 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 using 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 stated that the full variation of GPT-3 contained 175 billion criteria, [184] two orders of magnitude bigger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 designs with as couple of as 125 million criteria were likewise trained). [186]
OpenAI mentioned that GPT-3 succeeded at certain “meta-learning” tasks and could generalize the function of a single input-output pair. The GPT-3 release paper gave examples of translation and cross-linguistic transfer knowing between English and Romanian, and between English and German. [184]
GPT-3 drastically enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language models could be approaching or experiencing the basic ability constraints of predictive language models. [187] Pre-training GPT-3 needed numerous thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 model. [184] Like its predecessor, [174] the GPT-3 trained model was not right away launched to the public for concerns of possible abuse, although OpenAI prepared to permit gain access to through a paid cloud API after a two-month free personal 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 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 design can produce working code in over a dozen programming languages, many effectively in Python. [192]
Several issues with glitches, style flaws and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been accused of producing copyrighted code, with no 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 technology passed a simulated law school bar examination with a score around the leading 10% of test takers. (By contrast, GPT-3.5 scored around the bottom 10%.) They said that GPT-4 could also check out, examine or create approximately 25,000 words of text, and compose code in all major programming languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an improvement 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 decreased to reveal numerous technical details and stats 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 yewiki.org generate text, images and audio. [204] GPT-4o attained advanced outcomes in voice, multilingual, and vision criteria, setting new records in audio speech recognition and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) criteria compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI released GPT-4o mini, a smaller sized version 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 anticipates it to be especially useful for enterprises, startups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini models, which have been designed to take more time to consider their responses, resulting in greater precision. These models are especially efficient in science, coding, and thinking tasks, and were made available to ChatGPT Plus and Employee. [209] [210] In December 2024, o1-preview was changed by o1. [211]
o3

On December 20, wiki.snooze-hotelsoftware.de 2024, OpenAI unveiled o3, the follower of the o1 thinking design. OpenAI likewise revealed o3-mini, a lighter and quicker variation of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, security and security researchers had the opportunity to obtain early access to these models. [214] The design is called o3 rather than o2 to avoid confusion with telecommunications providers O2. [215]
Deep research study

Deep research study is a representative developed by OpenAI, unveiled on February 2, 2025. It leverages the capabilities of OpenAI’s o3 model to perform comprehensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With searching and Python tools allowed, 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 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 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 an unfortunate capybara”) and generate corresponding images. It can produce images of sensible things (“a stained-glass window with an image of a blue strawberry”) along with 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 version of the model with more realistic outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new rudimentary 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 effective model much better able to create images from complicated descriptions without manual prompt engineering and render complicated 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 design that can create videos based upon short detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution as much as 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora’s advancement team named it after the Japanese word for “sky”, to symbolize its “endless imaginative potential”. [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 along with copyrighted videos certified for that function, 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 might generate videos as much as one minute long. It likewise shared a technical report highlighting the approaches used to train the design, and the model’s capabilities. [225] It acknowledged a few of its drawbacks, consisting of battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos “outstanding”, however noted that they should 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 revealed significant interest in the technology’s potential. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the innovation’s capability to generate practical video from text descriptions, citing its potential to change storytelling and material development. He said that his excitement about Sora’s possibilities was so strong that he had decided to stop briefly plans for 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 large dataset of diverse audio and is also a multi-task model that can carry out multilingual speech acknowledgment in addition to 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 produce tunes with 10 instruments in 15 designs. According to The Verge, a tune produced by MuseNet tends to begin fairly but then fall into chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to create music for wiki.dulovic.tech the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to produce music with vocals. After training on 1.2 million samples, the system accepts a category, artist, and a bit of lyrics and outputs tune samples. OpenAI stated the songs “show regional musical coherence [and] follow standard chord patterns” however acknowledged that the songs lack “familiar larger musical structures such as choruses that repeat” which “there is a considerable space” in between Jukebox and human-generated music. The Verge stated “It’s technically impressive, even if the outcomes sound like mushy versions of tunes that may feel familiar”, systemcheck-wiki.de while Business Insider stated “remarkably, a few of the resulting tunes are memorable and sound genuine”. [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches makers to dispute toy problems in front of a human judge. The purpose is to research study whether such an approach might assist in auditing AI decisions and in developing 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 designs which are frequently studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks quickly. The models consisted of are AlexNet, VGG-19, various variations of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

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