Announced in 2016, Gym is an open-source Python library developed to assist in the advancement of reinforcement knowing algorithms. It aimed to standardize how environments are defined in AI research, making published research study more quickly reproducible [24] [144] while offering users with a simple user interface for interacting with these environments. In 2022, brand-new advancements of Gym have actually 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] using RL algorithms and research study generalization. Prior RL research focused mainly on enhancing representatives to fix single tasks. Gym Retro provides the capability to generalize between video games with comparable ideas but different 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, but are offered the goals of learning to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing procedure, trademarketclassifieds.com the representatives discover how to adapt to changing conditions. When a representative 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, suggesting it had actually discovered how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition between agents might create 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 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 skill level entirely through trial-and-error algorithms. Before ending up being a group of 5, the very first public demonstration took place at The International 2017, the yearly best championship tournament for the 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 2 weeks of actual time, and that the knowing software application was a step in the instructions of producing software that can handle complicated tasks like a surgeon. [152] [153] The system utilizes a kind of reinforcement knowing, as the bots discover over time by playing against themselves numerous 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 broadened to play together as a full team of 5, and they had the ability to defeat teams of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in two exhibition matches against expert players, however wound up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champs of the video game at the time, 2:0 in a live exhibit 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 mechanisms in Dota 2's bot gamer shows the obstacles of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has actually shown the use of deep support learning (DRL) representatives to attain superhuman proficiency 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 manipulate physical items. [167] It discovers completely in simulation using the exact same RL algorithms and training code as OpenAI Five. OpenAI tackled the item orientation issue by using domain randomization, a simulation method which exposes the student to a variety of experiences instead of attempting to fit to truth. The set-up for Dactyl, aside from having movement tracking video cameras, likewise has RGB video cameras to enable the robot to control an arbitrary 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 showed that Dactyl might solve a Rubik's Cube. The robotic had the ability to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complicated physics that is harder to model. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation technique of creating gradually more difficult environments. ADR differs from manual domain randomization by not needing a human to specify randomization varieties. [169]
API

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

The business has actually popularized generative pretrained transformers (GPT). [172]
OpenAI's initial GPT model ("GPT-1")

The initial paper on generative pre-training of a transformer-based language model was written by Alec Radford and his coworkers, and published in preprint on OpenAI's site on June 11, 2018. [173] It revealed how a generative design of language might obtain world knowledge and process long-range reliances 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 initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with just limited demonstrative versions initially launched to the public. The full variation of GPT-2 was not right away released due to concern about potential abuse, including applications for composing phony news. [174] Some specialists revealed uncertainty that GPT-2 presented a considerable risk.

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, alerted of "the innovation to completely fill Twitter, email, and the web up with reasonable-sounding, context-appropriate prose, which would drown out all other speech and be impossible to filter". [176] In November 2019, OpenAI released the complete version of the GPT-2 language model. [177] Several sites host interactive demonstrations of various circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language models to be general-purpose learners, illustrated by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the design 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 at least 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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 without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI stated that the full variation of GPT-3 contained 175 billion specifications, [184] two orders of magnitude larger than the 1.5 billion [185] in the full version of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were likewise trained). [186]
OpenAI specified that GPT-3 was successful at certain "meta-learning" jobs and might generalize the purpose 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 in 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 coming across the essential capability constraints of predictive language designs. [187] Pre-training GPT-3 required numerous 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 immediately 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 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 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 model can produce working code in over a dozen programming languages, a lot of successfully in Python. [192]
Several problems with glitches, design flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has been accused of discharging copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would cease support for trademarketclassifieds.com 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), capable of accepting text or image inputs. [199] They announced that the upgraded innovation 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 might also read, evaluate or create approximately 25,000 words of text, and write code in all significant shows languages. [200]
Observers reported that the model of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caveat that GPT-4 retained a few of the problems with earlier modifications. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually decreased to reveal various technical details and data about GPT-4, such as the accurate size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and released GPT-4o, which can process and create text, images and audio. [204] GPT-4o attained cutting edge results in voice, multilingual, and vision benchmarks, setting brand-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 launched GPT-4o mini, a smaller sized version 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 anticipates it to be particularly useful for business, 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 designs, which have actually been designed to take more time to consider their actions, leading to higher accuracy. These models are particularly efficient in science, coding, and reasoning jobs, 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 unveiled o3, the follower of the o1 thinking model. OpenAI likewise unveiled o3-mini, a lighter and faster version of OpenAI o3. Since 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 researchers had the opportunity to obtain early access to these designs. [214] The design is called o3 instead of o2 to avoid confusion with telecoms companies O2. [215]
Deep research study

Deep research study is a representative developed by OpenAI, revealed on February 2, 2025. It leverages the capabilities of OpenAI's o3 design to perform extensive web browsing, data analysis, and synthesis, delivering detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools allowed, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) criteria. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to analyze the semantic similarity in between text and images. It can significantly be utilized 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 uses a 12-billion-parameter version of GPT-3 to interpret natural language inputs (such as "a green leather bag shaped like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can create pictures of sensible objects ("a stained-glass window with a picture of a blue strawberry") as well as things 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 announced DALL-E 2, an updated variation of the design with more reasonable outcomes. [219] In December 2022, OpenAI released on GitHub software application for Point-E, a brand-new primary 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 powerful design much better able to produce images from intricate descriptions without manual timely engineering and render complex details like hands and text. [221] It was released to the general public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video model that can produce videos based upon short detailed triggers [223] as well as extend existing videos forwards or backwards in time. [224] It can generate videos with resolution up to 1920x1080 or 1080x1920. The optimum length of produced videos is unknown.

Sora's advancement group named it after the Japanese word for "sky", to symbolize its "endless creative potential". [223] Sora's innovation is an adaptation of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos as well as copyrighted videos accredited for that function, however did not expose the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, wiki.asexuality.org 2024, stating that it could create videos up to one minute long. It likewise shared a technical report highlighting the approaches utilized to train the design, and the model's abilities. [225] It acknowledged a few of its shortcomings, including battles imitating complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", but kept in mind that they need to have been cherry-picked and might not represent Sora's normal output. [225]
Despite uncertainty from some academic leaders following Sora's public demonstration, notable entertainment-industry figures have actually shown significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his astonishment at the innovation's capability to produce practical video from text descriptions, citing its possible to reinvent storytelling and content creation. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly plans for broadening his Atlanta-based motion picture 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 model that can perform multilingual speech acknowledgment 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 songs with 10 instruments in 15 styles. According to The Verge, a song generated by MuseNet tends to begin fairly however then fall into chaos the longer it plays. [230] [231] In pop culture, preliminary applications of this tool were utilized as early as 2020 for the web mental thriller Ben Drowned to create 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, and a bit of lyrics and outputs tune samples. OpenAI specified the songs "show local musical coherence [and] follow conventional chord patterns" however acknowledged that the tunes lack "familiar larger musical structures such as choruses that repeat" which "there is a considerable space" between Jukebox and human-generated music. The Verge mentioned "It's technically outstanding, even if the results sound like mushy versions of songs that might feel familiar", while Business Insider specified "remarkably, some of the resulting tunes are memorable and sound legitimate". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches makers to discuss toy problems in front of a human judge. The purpose is to research whether such an approach might help in auditing AI decisions 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 created to examine the functions that form inside these neural networks easily. The models included are AlexNet, VGG-19, different versions of Inception, and various variations of CLIP Resnet. [241]
ChatGPT

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