Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement learning algorithms. It aimed to standardize how environments are defined in AI research study, making published research more quickly reproducible [24] [144] while providing users with a simple user interface for communicating with these environments. In 2022, new developments of Gym have actually been moved to the library Gymnasium. [145] [146]
Gym Retro

Released in 2018, Gym Retro is a platform for reinforcement knowing (RL) research on computer game [147] using RL algorithms and research study generalization. Prior RL research study focused mainly on optimizing representatives to fix single tasks. Gym Retro offers the capability to generalize in between video games with comparable ideas but different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first do not have knowledge of how to even stroll, however are given the objectives of learning to move and to push the opposing agent out of the ring. [148] Through this adversarial learning procedure, the representatives learn how to adjust to changing conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the agent braces to remain upright, recommending it had found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives could create an intelligence "arms race" that might increase an agent'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 video game Dota 2, that learn to play against human gamers at a high skill level entirely through trial-and-error algorithms. Before becoming a group of 5, the very first public presentation occurred at The International 2017, the annual best champion tournament for trademarketclassifieds.com the game, where Dendi, an expert Ukrainian gamer, lost against a bot in a live one-on-one matchup. [150] [151] After the match, CTO Greg Brockman explained that the bot had discovered by playing against itself for two weeks of real time, which the knowing software was a step in the instructions of developing software application that can handle complicated tasks like a cosmetic surgeon. [152] [153] The system uses a form of reinforcement learning, as the bots discover gradually by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an enemy and taking map goals. [154] [155] [156]
By June 2018, the ability of the bots expanded to play together as a complete 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 two exhibit matches against professional players, however ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the ruling world champs of the video 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 overall 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 the usage of deep reinforcement learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker finding out to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers entirely in simulation utilizing the same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by utilizing domain randomization, a simulation approach which exposes the learner to a range of experiences instead of trying to fit to truth. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB electronic cameras to allow the robotic to control an arbitrary item by seeing it. In 2018, OpenAI revealed that the system was able to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl might fix a Rubik's Cube. The robot was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube present complex physics that is harder to design. 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 ranges. [169]
API

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

The business has actually promoted 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 coworkers, and published in preprint on OpenAI's website on June 11, 2018. [173] It showed 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 not being watched transformer language model and the successor to OpenAI's initial GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions at first released to the general public. The complete version of GPT-2 was not right away released due to issue about prospective misuse, including applications for composing fake news. [174] Some experts expressed uncertainty that GPT-2 posed a significant threat.

In action to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to discover "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 impossible to filter". [176] In November 2019, OpenAI released the total version of the GPT-2 language model. [177] Several websites host interactive demonstrations of various circumstances 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, shown by GPT-2 attaining cutting edge accuracy and perplexity on 7 of 8 zero-shot tasks (i.e. the model was not additional 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 prevents certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both specific 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 specified that the complete version of GPT-3 contained 175 billion criteria, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 models with as couple of as 125 million parameters were likewise trained). [186]
OpenAI stated that GPT-3 was successful at certain "meta-learning" jobs 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 between English and Romanian, and in between English and German. [184]
GPT-3 drastically enhanced benchmark outcomes over GPT-2. OpenAI warned that such scaling-up of language designs could be approaching or coming across the basic ability constraints of predictive language designs. [187] Pre-training GPT-3 needed a number of thousand petaflop/s-days [b] of compute, compared to tens of petaflop/s-days for the full GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design 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 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 in addition 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 create working code in over a dozen shows languages, the majority of effectively in Python. [192]
Several problems with problems, style flaws and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been implicated of releasing copyrighted code, without any author attribution or it-viking.ch license. [197]
OpenAI revealed that they would cease assistance 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 test 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 read, analyze or generate up to 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the iteration of ChatGPT utilizing GPT-4 was an enhancement 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 efficient in taking images as input on ChatGPT. [202] OpenAI has decreased to expose different technical details and data 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 modern 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) standard 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 particularly helpful for business, start-ups and designers looking for to automate services with AI agents. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have actually been developed to take more time to think of their responses, causing greater precision. These models are especially 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 revealed o3, the follower of the o1 thinking model. OpenAI likewise revealed o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this design 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 researchers had the chance to obtain early access to these designs. [214] The design is called o3 instead of o2 to prevent confusion with telecoms companies O2. [215]
Deep research study

Deep research study is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 design to perform extensive web browsing, information 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) benchmark. [120]
Image classification

CLIP

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

DALL-E

Revealed in 2021, DALL-E is a Transformer model that produces images from textual descriptions. [218] DALL-E uses a 12-billion-parameter variation of GPT-3 to interpret natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can produce pictures of sensible items ("a stained-glass window with an image of a blue strawberry") as well as 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 announced DALL-E 2, an updated version of the model with more sensible results. [219] In December 2022, OpenAI published on GitHub software for wiki.eqoarevival.com Point-E, a brand-new rudimentary system for converting a text description into a 3-dimensional model. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful model better able to generate images from intricate descriptions without manual prompt 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 in reverse in time. [224] It can create videos with resolution up to 1920x1080 or 1080x1920. The maximal length of produced videos is unknown.

Sora's development group called it after the Japanese word for "sky", to signify its "limitless imaginative potential". [223] Sora's technology is an adjustment of the innovation behind the DALL · E 3 text-to-image design. [225] OpenAI trained the system using publicly-available videos along with copyrighted videos accredited for that function, however did not reveal the number or the specific sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, stating that it could create videos approximately one minute long. It likewise shared a technical report highlighting the approaches utilized to train the model, and the model's abilities. [225] It acknowledged some of its imperfections, consisting of struggles imitating intricate physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "impressive", 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 scholastic leaders following Sora's public demonstration, significant entertainment-industry figures have actually shown substantial interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's capability to generate sensible video from text descriptions, citing its possible to change storytelling and material development. He said that his excitement about Sora's possibilities was so strong that he had chosen to pause prepare for expanding 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 likewise a multi-task design that can carry out multilingual speech acknowledgment along with speech translation and language identification. [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 create tunes with 10 instruments in 15 designs. According to The Verge, a song produced by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In pop culture, initial applications of this tool were utilized as early as 2020 for the web psychological thriller Ben Drowned to produce music for 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 snippet of lyrics and samples. OpenAI mentioned the tunes "reveal regional musical coherence [and] follow standard chord patterns" however acknowledged that the songs do not have "familiar larger musical structures such as choruses that repeat" and that "there is a significant gap" between Jukebox and human-generated music. The Verge mentioned "It's technologically impressive, even if the results sound like mushy variations of songs that may feel familiar", while Business Insider stated "remarkably, some of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches machines to discuss toy issues in front of a human judge. The purpose is to research study whether such a method may 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 considerable layer and neuron of eight neural network designs which are frequently studied in interpretability. [240] Microscope was produced to analyze the features that form inside these neural networks quickly. The models included are AlexNet, VGG-19, various versions of Inception, and various versions of CLIP Resnet. [241]
ChatGPT

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