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

Released in 2018, Gym Retro is a platform for support knowing (RL) research study on computer game [147] utilizing RL algorithms and study generalization. Prior RL research study focused mainly on enhancing representatives to solve single tasks. Gym Retro provides the ability to generalize between games with similar concepts but various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives initially lack understanding of how to even stroll, however are given the objectives of discovering to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adjust to altering conditions. When a representative is then removed from this virtual environment and put in a brand-new virtual environment with high winds, the representative 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 agents could produce an intelligence "arms race" that could increase a representative's ability to operate even outside the context of the competitors. [148]
OpenAI 5

OpenAI Five is a team of five 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 skill level entirely through experimental algorithms. Before ending up being a team of 5, the very first public demonstration took place at The International 2017, the annual premiere championship competition for the video game, where Dendi, a professional Ukrainian player, lost against a bot in a live individually 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 was an action in the instructions of developing software that can handle complex jobs like a cosmetic surgeon. [152] [153] The system utilizes a type of support learning, higgledy-piggledy.xyz as the bots learn in time by playing against themselves hundreds of 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 complete team of 5, and they had the ability to beat teams of amateur and semi-professional gamers. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibition matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated OG, the reigning world champs of the video 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 overall video games in a four-day open online competitors, winning 99.4% of those games. [165]
OpenAI 5's mechanisms in Dota 2's bot gamer reveals the obstacles of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated using deep reinforcement knowing (DRL) agents to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses machine finding out to train a Shadow Hand, a human-like robot hand, to control physical objects. [167] It discovers entirely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by utilizing domain randomization, a simulation approach which exposes the student to a variety of experiences rather than attempting to fit to reality. The set-up for Dactyl, aside from having motion tracking cameras, also has RGB cams to allow the robot to manipulate an approximate things by seeing it. In 2018, OpenAI showed that the system had the ability to manipulate a cube and wiki.dulovic.tech an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could solve a Rubik's Cube. The robotic was able to resolve the puzzle 60% of the time. Objects like the Rubik's Cube introduce 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 creating progressively harder environments. ADR differs from manual domain randomization by not requiring a human to specify randomization ranges. [169]
API

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

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

The original paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world knowledge and procedure long-range dependences by pre-training on a diverse corpus with long stretches of contiguous text.

GPT-2

Generative Pre-trained Transformer 2 ("GPT-2") is an unsupervised transformer language model and the follower to OpenAI's original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only restricted demonstrative versions at first launched to the general public. The full variation of GPT-2 was not immediately released due to concern about possible abuse, consisting of applications for writing fake news. [174] Some experts revealed uncertainty that GPT-2 posed a significant hazard.

In response to GPT-2, the Allen Institute for Artificial Intelligence reacted with a tool to identify "neural phony news". [175] Other researchers, such as Jeremy Howard, alerted 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 difficult to filter". [176] In November 2019, OpenAI launched the complete version of the GPT-2 language design. [177] Several websites host interactive demonstrations 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, shown by GPT-2 attaining modern 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 somewhat 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain concerns encoding vocabulary with word tokens by utilizing byte pair encoding. This allows 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 an unsupervised transformer language model and the follower 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 also 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 in between English and Romanian, and in between English and German. [184]
GPT-3 considerably enhanced benchmark results over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or coming across the fundamental ability constraints of predictive language designs. [187] Pre-training GPT-3 needed several thousand petaflop/s-days [b] of calculate, compared to 10s of petaflop/s-days for the complete GPT-2 design. [184] Like its predecessor, [174] the GPT-3 trained design was not immediately launched to the public for concerns of possible abuse, although OpenAI prepared to allow gain access to through a paid cloud API after a two-month free private beta that began in June 2020. [170] [189]
On September 23, 2020, GPT-3 was licensed specifically 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 personal beta. [194] According to OpenAI, yewiki.org the design can create working code in over a lots shows languages, most successfully in Python. [192]
Several issues with glitches, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been implicated of giving off copyrighted code, without any author attribution or license. [197]
OpenAI announced that they would terminate assistance 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), capable of accepting text or image inputs. [199] They revealed 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 also read, evaluate or produce up to 25,000 words of text, and write code in all significant programs languages. [200]
Observers reported that the model of ChatGPT using GPT-4 was an enhancement on the previous GPT-3.5-based version, with the caveat that GPT-4 retained a few of the issues with earlier revisions. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different technical details and statistics about GPT-4, such as the precise size of the model. [203]
GPT-4o

On May 13, 2024, OpenAI announced and launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained modern outcomes in voice, multilingual, and vision standards, setting new records in audio speech acknowledgment 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 variation 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 anticipates it to be especially useful for business, 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 actually been developed to take more time to consider their responses, resulting in greater accuracy. These models are especially reliable in science, coding, and thinking jobs, 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, 2024, OpenAI revealed o3, the successor of the o1 thinking model. OpenAI also 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 wiki.vst.hs-furtwangen.de o3-mini. [212] [213] Until January 10, 2025, safety and security scientists had the chance to obtain early access to these designs. [214] The model is called o3 instead of o2 to avoid confusion with telecommunications companies O2. [215]
Deep research

Deep research study is an agent established by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web surfing, wiki.lafabriquedelalogistique.fr data analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 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) criteria. [120]
Image classification

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a design that is trained to examine the semantic similarity in 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 design that creates images from textual descriptions. [218] DALL-E utilizes a 12-billion-parameter variation of GPT-3 to analyze natural language inputs (such as "a green leather purse shaped like a pentagon" or "an isometric view of an unfortunate capybara") and generate corresponding images. It can create pictures of practical things ("a stained-glass window with a picture of a blue strawberry") in addition to 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 variation of the model with more realistic results. [219] In December 2022, OpenAI released on GitHub software for Point-E, a new simple system for converting a text description into a 3-dimensional design. [220]
DALL-E 3

In September 2023, OpenAI announced DALL-E 3, a more effective design much 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 public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can generate videos based upon short detailed triggers [223] in addition to extend existing videos forwards or backwards in time. [224] It can create videos with resolution approximately 1920x1080 or larsaluarna.se 1080x1920. The maximal length of created videos is unidentified.

Sora's development group named it after the Japanese word for "sky", to signify its "endless creative capacity". [223] Sora's technology is an adjustment of the technology behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos certified for that purpose, however did not expose the number or the exact sources of the videos. [223]
OpenAI demonstrated some Sora-created high-definition videos to the general public on February 15, 2024, stating that it could create videos up to one minute long. It likewise shared a technical report highlighting the techniques used to train the design, and the design's capabilities. [225] It acknowledged some of its imperfections, including battles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "outstanding", however noted 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 demo, notable entertainment-industry figures have shown substantial interest in the innovation's capacity. In an interview, actor/filmmaker Tyler Perry revealed his astonishment at the technology's capability to generate realistic video from text descriptions, citing its potential to transform storytelling and material production. He said that his enjoyment about Sora's possibilities was so strong that he had decided to stop briefly prepare for broadening his Atlanta-based motion picture studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech recognition model. [228] It is trained on a large dataset of varied audio and is likewise a multi-task model that can carry out multilingual speech recognition 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 create songs with 10 instruments in 15 designs. According to The Verge, a song created by MuseNet tends to start fairly but then fall under mayhem the longer it plays. [230] [231] In popular culture, initial applications of this tool were utilized as early as 2020 for yewiki.org the internet mental thriller Ben Drowned to create music for the titular character. [232] [233]
Jukebox

Released in 2020, Jukebox is an open-sourced algorithm to create 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 mentioned the tunes "reveal regional musical coherence [and] follow standard chord patterns" but acknowledged that the songs lack "familiar larger musical structures such as choruses that duplicate" which "there is a considerable gap" in between Jukebox and human-generated music. The Verge specified "It's technically excellent, even if the results sound like mushy versions of tunes that might feel familiar", while Business Insider specified "surprisingly, a few of the resulting tunes are memorable and sound genuine". [234] [235] [236]
User interfaces

Debate Game

In 2018, OpenAI released the Debate Game, which teaches machines to discuss toy problems in front of a human judge. The purpose is to research whether such a technique might 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 nerve cell of eight neural network designs which are typically studied in interpretability. [240] Microscope was produced to evaluate the functions that form inside these neural networks easily. The models consisted of are AlexNet, VGG-19, various variations of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

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