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

Released in 2018, Gym Retro is a platform for reinforcement learning (RL) research study on video games [147] utilizing RL algorithms and study generalization. Prior RL research focused mainly on enhancing representatives to solve single jobs. Gym Retro provides the capability to generalize in between games with comparable ideas however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic representatives at first lack understanding of how to even stroll, but are offered the objectives of finding out to move and to push the opposing representative out of the ring. [148] Through this adversarial knowing process, the representatives find out how to adjust to changing conditions. When an agent is then eliminated from this virtual environment and placed in a new virtual environment with high winds, the agent braces to remain upright, suggesting it had found out how to stabilize in a generalized method. [148] [149] OpenAI's Igor Mordatch argued that competitors between agents could develop an intelligence "arms race" that might increase an agent's ability to operate 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 video game Dota 2, that discover to play against human players at a high skill level completely through trial-and-error algorithms. Before ending up being a group of 5, the first public demonstration took place at The International 2017, the yearly best championship competition for the video game, where Dendi, a professional 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 actually discovered by playing against itself for 2 weeks of real time, and that the knowing software was a step in the direction of producing software application that can handle complicated tasks like a cosmetic surgeon. [152] [153] The system utilizes a type of reinforcement learning, as the bots learn in 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 were able to beat groups of amateur and semi-professional players. [157] [154] [158] [159] At The International 2018, OpenAI Five played in 2 exhibit matches against professional gamers, but wound up losing both video games. [160] [161] [162] In April 2019, OpenAI Five beat OG, the ruling world champions 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 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 making use of deep support learning (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl uses device discovering to train a Shadow Hand, a human-like robotic hand, to control physical things. [167] It finds out completely in simulation utilizing the very same RL algorithms and training code as OpenAI Five. OpenAI tackled the things orientation problem by using domain randomization, a simulation technique which exposes the learner to a variety of experiences instead of 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 approximate object by seeing it. In 2018, disgaeawiki.info OpenAI showed that the system had the ability to control a cube and an octagonal prism. [168]
In 2019, OpenAI showed that Dactyl could resolve a Rubik's Cube. The robotic had the ability to solve the puzzle 60% of the time. Objects like the Rubik's Cube introduce complicated physics that is harder to model. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation technique of generating gradually harder environments. ADR differs from manual domain randomization by not requiring a human to specify randomization ranges. [169]
API

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

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

The initial paper on generative pre-training of a transformer-based language model was composed by Alec Radford and his associates, and released in preprint on OpenAI's site on June 11, 2018. [173] It demonstrated how a generative design of language might obtain world knowledge and procedure long-range dependencies by pre-training on a varied 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 original GPT model ("GPT-1"). GPT-2 was announced in February 2019, with only minimal demonstrative versions initially released to the public. The complete variation of GPT-2 was not immediately launched due to issue about possible abuse, consisting of applications for writing phony news. [174] Some specialists expressed uncertainty that GPT-2 postured a significant danger.

In reaction to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to spot "neural phony news". [175] Other researchers, 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 variation of the GPT-2 language design. [177] Several sites host interactive demonstrations of various instances of GPT-2 and other transformer models. [178] [179] [180]
GPT-2's authors argue not being watched language models to be general-purpose learners, gratisafhalen.be shown by GPT-2 attaining advanced precision and perplexity on 7 of 8 zero-shot tasks (i.e. the design was not more 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 avoids certain problems 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 an unsupervised transformer language model and the successor to GPT-2. [182] [183] [184] OpenAI stated 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 variation of GPT-2 (although GPT-3 models with as couple of as 125 million criteria were likewise trained). [186]
OpenAI specified that GPT-3 prospered 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 between English and German. [184]
GPT-3 dramatically enhanced benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs might be approaching or encountering the fundamental capability constraints of predictive language models. [187] Pre-training GPT-3 needed a number of 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 design was not immediately released to the general public for issues of possible abuse, although OpenAI planned to allow gain access to through a paid cloud API after a two-month totally free personal beta that started in June 2020. [170] [189]
On September 23, 2020, GPT-3 was certified solely 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 personal beta. [194] According to OpenAI, the design can produce working code in over a dozen programs languages, the majority of successfully in Python. [192]
Several concerns with problems, design defects and security vulnerabilities were pointed out. [195] [196]
GitHub Copilot has actually been accused of producing copyrighted code, with no author attribution or license. [197]
OpenAI revealed 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 test with a score 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, evaluate or create approximately 25,000 words of text, and compose code in all major programs languages. [200]
Observers reported that the version of ChatGPT utilizing GPT-4 was an enhancement on the previous GPT-3.5-based iteration, with the caveat that GPT-4 retained some of the issues with earlier modifications. [201] GPT-4 is likewise capable of taking images as input on ChatGPT. [202] OpenAI has decreased to reveal different 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 launched GPT-4o, which can process and produce text, images and audio. [204] GPT-4o attained cutting edge results in voice, bytes-the-dust.com multilingual, and vision criteria, setting new records in audio speech acknowledgment and translation. [205] [206] It scored 88.7% on the Massive Multitask Language Understanding (MMLU) benchmark compared to 86.5% by GPT-4. [207]
On July 18, 2024, OpenAI launched 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 particularly useful for enterprises, start-ups and developers seeking to automate services with AI representatives. [208]
o1

On September 12, 2024, OpenAI released the o1-preview and o1-mini designs, which have been created to take more time to think about their reactions, leading to greater accuracy. These designs are particularly effective in science, coding, and thinking jobs, and wiki.rolandradio.net were made available to ChatGPT Plus and Team members. [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 also unveiled o3-mini, a lighter and much faster variation 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, disgaeawiki.info security and security scientists had the opportunity to obtain early access to these models. [214] The design is called o3 instead of o2 to prevent confusion with telecoms companies O2. [215]
Deep research

Deep research is an agent established by OpenAI, revealed on February 2, 2025. It leverages the abilities of OpenAI's o3 model to perform substantial web surfing, information analysis, and synthesis, providing detailed reports within a timeframe of 5 to 30 minutes. [216] With browsing and Python tools made it possible for, it reached an accuracy of 26.6 percent on HLE (Humanity's Last Exam) standard. [120]
Image category

CLIP

Revealed in 2021, CLIP (Contrastive Language-Image Pre-training) is a model that is trained to examine the semantic similarity in between text and images. It can notably 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 variation of GPT-3 to interpret natural language inputs (such as "a green leather handbag formed like a pentagon" or "an isometric view of an unfortunate capybara") and produce corresponding images. It can develop pictures of realistic 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, disgaeawiki.info no API or code is available.

DALL-E 2

In April 2022, OpenAI announced DALL-E 2, an updated variation of the model with more realistic results. [219] In December 2022, OpenAI published on GitHub software for Point-E, a new fundamental 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 powerful model much better able to generate images from complex descriptions without manual timely engineering and render intricate details like hands and text. [221] It was released to the public as a ChatGPT Plus feature in October. [222]
Text-to-video

Sora

Sora is a text-to-video design that can produce videos based on short detailed prompts [223] in addition to extend existing videos forwards or backwards in time. [224] It can generate videos with resolution as much as 1920x1080 or 1080x1920. The maximal length of generated videos is unidentified.

Sora's advancement group called it after the Japanese word for "sky", to symbolize its "endless imaginative potential". [223] Sora's innovation is an adjustment of the innovation behind the DALL · E 3 text-to-image model. [225] OpenAI trained the system utilizing publicly-available videos in addition to copyrighted videos licensed for wiki.snooze-hotelsoftware.de that purpose, but did not expose the number or the precise sources of the videos. [223]
OpenAI showed some Sora-created high-definition videos to the general public on February 15, 2024, stating that it might create videos as much as one minute long. It likewise shared a technical report highlighting the techniques used to train the model, and the design's abilities. [225] It acknowledged a few of its shortcomings, including battles mimicing complex physics. [226] Will Douglas Heaven of the MIT Technology Review called the presentation videos "excellent", but noted that they must 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 actually revealed significant interest in the technology's capacity. In an interview, actor/filmmaker Tyler Perry expressed his awe at the innovation's ability to generate realistic video from text descriptions, mentioning its prospective to change storytelling and material production. He said that his excitement about Sora's possibilities was so strong that he had actually chosen to pause plans for expanding his Atlanta-based film studio. [227]
Speech-to-text

Whisper

Released in 2022, Whisper is a general-purpose speech acknowledgment model. [228] It is trained on a big dataset of varied audio and is likewise a multi-task design that can perform multilingual speech acknowledgment as well as speech translation and language recognition. [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 tunes 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 popular culture, preliminary applications of this tool were utilized as early as 2020 for the internet psychological thriller Ben Drowned to develop 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 bit of lyrics and outputs tune samples. OpenAI mentioned the songs "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes lack "familiar larger musical structures such as choruses that duplicate" and that "there is a significant gap" 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 Business Insider mentioned "remarkably, a few of the resulting tunes are appealing and sound genuine". [234] [235] [236]
User user interfaces

Debate Game

In 2018, OpenAI launched 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 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 typically studied in interpretability. [240] Microscope was created to analyze the functions that form inside these neural networks quickly. The designs consisted of are AlexNet, VGG-19, various versions of Inception, and different versions of CLIP Resnet. [241]
ChatGPT

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