Announced in 2016, Gym is an open-source Python library developed to assist in the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research, wiki.whenparked.com making published research study more quickly reproducible [24] [144] while offering users with a basic interface for connecting with these environments. In 2022, new advancements of Gym have actually been moved 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] using RL algorithms and study generalization. Prior RL research study focused mainly on optimizing representatives to solve single tasks. Gym Retro offers the ability to generalize between video games with similar principles but different looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robot representatives at first do not have understanding of how to even stroll, but are given the objectives of finding out to move and to press the opposing representative out of the ring. [148] Through this adversarial learning process, the agents find out how to adjust to altering conditions. When a representative is then removed from this virtual environment and positioned in a new virtual environment with high winds, the representative braces to remain upright, recommending it had actually found out how to balance in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives could produce an intelligence "arms race" that might increase a representative's ability to operate even outside the context of the competition. [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 learn to play against human gamers at a high skill level totally through trial-and-error algorithms. Before becoming a group of 5, the first public demonstration took place at The International 2017, the yearly best champion competition for the video game, where Dendi, a professional Ukrainian gamer, lost against a bot in a live individually match. [150] [151] After the match, CTO Greg Brockman explained that the bot had actually learned by playing against itself for two weeks of actual time, and that the learning software was a step in the instructions of creating software that can handle intricate jobs like a cosmetic surgeon. [152] [153] The system uses a type of reinforcement learning, as the bots find out over time by playing against themselves numerous times a day for months, and are rewarded for actions such as eliminating an opponent and taking map objectives. [154] [155] [156]
By June 2018, the capability of the bots expanded to play together as a complete group of 5, and they had the ability to beat groups 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 ended up losing both games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions of the game at the time, 2:0 in a live exhibit match in San Francisco. [163] [164] The bots' final public look came later on that month, where they played in 42,729 total games in a four-day open online competitors, winning 99.4% of those video games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the difficulties of AI systems in multiplayer online battle arena (MOBA) video games and how OpenAI Five has demonstrated the usage of deep support learning (DRL) representatives to attain superhuman skills in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes maker discovering to train a Shadow Hand, a human-like robotic hand, to manipulate physical things. [167] It learns totally in simulation using the same RL algorithms and training code as OpenAI Five. OpenAI took on the item orientation issue by utilizing domain randomization, a simulation method which exposes the student to a range of experiences rather than trying to fit to reality. The set-up for Dactyl, aside from having motion tracking cams, likewise has RGB cams to enable the robot to manipulate an arbitrary item by seeing it. In 2018, OpenAI showed that the system was able to manipulate 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 solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce intricate physics that is harder to design. OpenAI did this by enhancing the toughness of Dactyl to perturbations by using Automatic Domain Randomization (ADR), a simulation method of generating gradually more difficult 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 developed 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 initial GPT model ("GPT-1")

The original paper on generative pre-training of a transformer-based language design was composed by Alec Radford and his colleagues, and released in preprint on OpenAI's website on June 11, systemcheck-wiki.de 2018. [173] It demonstrated how a generative design of language might obtain world understanding and process long-range dependences by pre-training on a diverse corpus with long stretches of adjoining text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's initial GPT design ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions at first launched to the public. The complete variation of GPT-2 was not instantly released due to concern about prospective misuse, including applications for composing fake news. [174] Some experts revealed uncertainty that GPT-2 positioned a considerable threat.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to detect "neural phony news". [175] Other researchers, such as Jeremy Howard, warned of "the technology 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 total variation of the GPT-2 language design. [177] Several websites host interactive demonstrations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue without supervision language models to be general-purpose learners, shown by GPT-2 attaining state-of-the-art precision and perplexity on 7 of 8 zero-shot tasks (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 issues encoding vocabulary with word tokens by utilizing byte pair encoding. This permits representing any string of characters by encoding both individual 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 mentioned that the complete variation of GPT-3 contained 175 billion specifications, [184] 2 orders of magnitude larger than the 1.5 billion [185] in the complete version of GPT-2 (although GPT-3 designs with as few as 125 million specifications were likewise trained). [186]
OpenAI specified that GPT-3 was successful at certain "meta-learning" tasks and could generalize the purpose of a single input-output pair. The GPT-3 release paper offered examples of translation and cross-linguistic transfer learning between English and Romanian, and in between English and German. [184]
GPT-3 considerably improved benchmark results over GPT-2. OpenAI warned that such scaling-up of language designs might be approaching or coming across the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 required numerous 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 model was not immediately launched to the general public for issues of possible abuse, although OpenAI planned to enable gain access to through a paid cloud API after a two-month totally free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified specifically 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 design can create working code in over a lots programs languages, many effectively in Python. [192]
Several issues with problems, style defects and security vulnerabilities were cited. [195] [196]
GitHub Copilot has been accused of giving off copyrighted code, with no author attribution or license. [197]
OpenAI announced that they would discontinue support 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 exam 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 likewise check out, analyze or create up to 25,000 words of text, and write code in all significant programming languages. [200]
Observers reported that the version of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based model, with the caution that GPT-4 retained some of the problems with earlier revisions. [201] GPT-4 is also efficient in taking images as input on ChatGPT. [202] OpenAI has actually declined to reveal numerous technical details and statistics about GPT-4, such as the exact 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 advanced lead to voice, multilingual, and vision benchmarks, setting 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 version of GPT-4o replacing GPT-3.5 Turbo on the ChatGPT 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 business, startups and developers 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 developed to take more time to think about their actions, causing greater accuracy. These models are especially efficient in science, coding, and reasoning jobs, and were made available to ChatGPT Plus and Staff member. [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 design. OpenAI likewise unveiled o3-mini, a lighter and faster version of OpenAI o3. Since December 21, 2024, this model is not available for public use. According to OpenAI, they are checking o3 and o3-mini. [212] [213] Until January 10, 2025, safety and security researchers had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to prevent confusion with telecoms providers O2. [215]
Deep research study

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to carry out extensive web browsing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools enabled, it reached a precision 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 evaluate the semantic similarity 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 variation of GPT-3 to interpret natural language inputs (such as "a green leather bag formed like a pentagon" or "an isometric view of a sad capybara") and produce corresponding images. It can develop pictures of practical 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"). Since March 2021, no API or code is available.

DALL-E 2

In April 2022, OpenAI revealed DALL-E 2, an upgraded version of the model with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software application for Point-E, a brand-new basic system for transforming a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective model much better able to generate 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 feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can create videos based on brief detailed triggers [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 unidentified.

Sora's development group named it after the Japanese word for "sky", to symbolize its "unlimited creative capacity". [223] Sora's technology is an adjustment 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 exact sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the public on February 15, 2024, specifying that it could generate videos approximately one minute long. It likewise shared a technical report highlighting the methods used to train the design, and the model's capabilities. [225] It acknowledged a few of its imperfections, including struggles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", however kept in mind that they must have been cherry-picked and might not represent Sora's typical output. [225]
Despite uncertainty from some academic leaders following Sora's public demo, significant entertainment-industry figures have actually shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry revealed his awe at the technology's capability to create sensible video from text descriptions, citing its potential to reinvent storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had actually chosen to stop briefly strategies 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 diverse audio and is also a multi-task design 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 start 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 internet mental thriller Ben Drowned to produce music for the titular character. [232] [233]
Jukebox

Released in 2020, 89u89.com 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 snippet of lyrics and outputs song samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow conventional 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" in between Jukebox and human-generated music. The Verge stated "It's technologically outstanding, even if the outcomes seem like mushy variations of tunes that might feel familiar", while stated "surprisingly, a few of the resulting tunes are appealing and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI introduced the Debate Game, which teaches machines to dispute toy issues in front of a human judge. The purpose is to research study whether such an approach may help in auditing AI decisions and in developing explainable AI. [237] [238]
Microscope

Released in 2020, Microscope [239] is a collection of visualizations of every considerable layer and neuron of 8 neural network designs which are frequently studied in interpretability. [240] Microscope was developed to evaluate the features that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and different variations of CLIP Resnet. [241]
ChatGPT

Launched in November 2022, ChatGPT is a synthetic intelligence tool constructed on top of GPT-3 that provides a conversational user interface that enables users to ask questions in natural language. The system then responds with a response within seconds.