GitLab

Announced in 2016, Gym is an open-source Python library designed to facilitate the development of reinforcement knowing algorithms. It aimed to standardize how environments are specified in AI research study, making released research study more easily 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 relocated 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] using RL algorithms and research study generalization. Prior RL research focused mainly on enhancing agents to fix single tasks. Gym Retro provides the ability to generalize in between games with similar concepts however various looks.

RoboSumo

Released in 2017, RoboSumo is a virtual world where humanoid metalearning robotic agents initially lack knowledge of how to even stroll, but are offered the goals of finding out to move and to push the opposing agent out of the ring. [148] Through this adversarial knowing procedure, the agents discover how to adjust to changing conditions. When an agent 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, recommending it had actually discovered how to stabilize in a generalized way. [148] [149] OpenAI's Igor Mordatch argued that competition in between representatives might produce an intelligence "arms race" that might increase an agent's capability to function even outside the context of the competition. [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 discover to play against human gamers at a high skill level totally through experimental algorithms. Before ending up being a group of 5, the first public demonstration happened at The International 2017, the annual premiere championship tournament for the video game, where Dendi, an expert Ukrainian gamer, trademarketclassifieds.com 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 found out by playing against itself for 2 weeks of genuine time, and that the knowing software was a step in the instructions of producing software that can handle intricate jobs like a surgeon. [152] [153] The system utilizes a form of reinforcement learning, as the bots find out over time by playing against themselves hundreds of 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 full team of 5, and they were able to beat groups 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 ended up losing both video games. [160] [161] [162] In April 2019, OpenAI Five defeated 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' 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 video games. [165]
OpenAI 5's systems in Dota 2's bot player shows the difficulties of AI systems in multiplayer online fight arena (MOBA) video games and how OpenAI Five has demonstrated using deep reinforcement knowing (DRL) representatives to attain superhuman competence in Dota 2 matches. [166]
Dactyl

Developed in 2018, Dactyl utilizes device discovering to train a Shadow Hand, a human-like robot hand, to control physical things. [167] It discovers completely in simulation utilizing the exact same RL algorithms and training code as OpenAI Five. OpenAI took on the things orientation issue by using domain randomization, a simulation method which exposes the learner to a variety of experiences instead of attempting to fit to reality. The set-up for Dactyl, aside from having movement tracking electronic cameras, also has RGB cams to permit the robot to manipulate an arbitrary object by seeing it. In 2018, OpenAI revealed that the system was able to manipulate a cube and an octagonal prism. [168]
In 2019, OpenAI demonstrated that Dactyl could fix a Rubik's Cube. The robot was able to fix the puzzle 60% of the time. Objects like the Rubik's Cube present intricate physics that is harder to design. OpenAI did this by improving the robustness of Dactyl to perturbations by utilizing Automatic Domain Randomization (ADR), a simulation method of creating progressively more challenging environments. ADR varies from manual domain randomization by not needing a human to define 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 developers get in touch with it for "any English language AI job". [170] [171]
Text generation

The company has 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 colleagues, and published in preprint on OpenAI's website on June 11, 2018. [173] It demonstrated how a generative model of language could 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 design and the successor to OpenAI's original GPT design ("GPT-1"). GPT-2 was revealed in February 2019, with only minimal demonstrative versions at first launched to the public. The complete version of GPT-2 was not instantly released due to issue about potential misuse, including applications for composing phony news. [174] Some experts revealed uncertainty that GPT-2 posed a significant risk.

In response to GPT-2, the Allen Institute for Artificial Intelligence responded with a tool to identify "neural phony news". [175] Other scientists, 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 complete variation of the GPT-2 language model. [177] Several websites host interactive presentations of different circumstances of GPT-2 and other transformer designs. [178] [179] [180]
GPT-2's authors argue unsupervised language designs to be general-purpose students, shown by GPT-2 attaining state-of-the-art accuracy and perplexity on 7 of 8 zero-shot jobs (i.e. the design was not additional trained on any task-specific input-output examples).

The corpus it was trained on, called WebText, bio.rogstecnologia.com.br contains slightly 40 gigabytes of text from URLs shared in Reddit submissions with a minimum of 3 upvotes. It avoids certain problems encoding vocabulary with word tokens by using byte pair encoding. This permits 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 a without supervision transformer language model and the follower to GPT-2. [182] [183] [184] OpenAI mentioned that the complete version of GPT-3 contained 175 billion parameters, [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 few as 125 million criteria were also trained). [186]
OpenAI mentioned that GPT-3 prospered at certain "meta-learning" tasks and might generalize the function 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 between English and German. [184]
GPT-3 considerably improved benchmark outcomes over GPT-2. OpenAI cautioned that such scaling-up of language designs could be approaching or encountering the essential ability constraints of predictive language models. [187] Pre-training GPT-3 required numerous thousand petaflop/s-days [b] of compute, compared to 10s of petaflop/s-days for the full GPT-2 design. [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 personal beta that began 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 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, the model can produce working code in over a lots shows languages, a lot of efficiently in Python. [192]
Several concerns with problems, style flaws and security vulnerabilities were cited. [195] [196]
GitHub Copilot has actually been implicated of discharging copyrighted code, with no author attribution or license. [197]
OpenAI revealed that they would discontinue 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), efficient in accepting text or image inputs. [199] They revealed that the upgraded innovation 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 could also read, examine or produce up to 25,000 words of text, and compose 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 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 declined to reveal various technical details and stats about GPT-4, such as the accurate size of the design. [203]
GPT-4o

On May 13, 2024, OpenAI revealed and released GPT-4o, which can process and produce text, engel-und-waisen.de images and audio. [204] GPT-4o attained cutting edge lead to voice, multilingual, and vision criteria, setting new records in audio speech recognition 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 released GPT-4o mini, a smaller sized variation of GPT-4o changing 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 particularly useful for enterprises, 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 designs, which have been developed to take more time to believe about their reactions, causing higher accuracy. These models are particularly 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 model. OpenAI likewise revealed o3-mini, gratisafhalen.be a lighter and much faster version of OpenAI o3. Since December 21, 2024, this model is not available for public usage. According to OpenAI, they are testing o3 and systemcheck-wiki.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 design is called o3 rather than o2 to avoid confusion with telecoms providers O2. [215]
Deep research study

Deep research is an agent developed by OpenAI, unveiled on February 2, 2025. It leverages the abilities of OpenAI's o3 design 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 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 model that is trained to analyze the semantic similarity between text and images. It can significantly be used for image category. [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 version 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 matching images. It can develop images of practical items ("a stained-glass window with a picture of a blue strawberry") along with 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 revealed DALL-E 2, an upgraded variation of the design with more reasonable outcomes. [219] In December 2022, OpenAI published on GitHub software for Point-E, a brand-new fundamental system for converting a text description into a 3 design. [220]
DALL-E 3

In September 2023, OpenAI revealed DALL-E 3, a more powerful design better able to generate images from complicated descriptions without manual timely engineering and render complicated details like hands and text. [221] It was launched to the public as a ChatGPT Plus function in October. [222]
Text-to-video

Sora

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

Sora's advancement team called it after the Japanese word for "sky", to represent its "limitless innovative 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 utilizing publicly-available videos as well as copyrighted videos licensed for that function, but did not expose 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, it-viking.ch mentioning that it might create videos up to one minute long. It also shared a technical report highlighting the approaches utilized to train the model, and the model's abilities. [225] It acknowledged some of its shortcomings, consisting of struggles mimicing complicated physics. [226] Will Douglas Heaven of the MIT Technology Review called the demonstration videos "outstanding", however noted that they must have been cherry-picked and may not represent Sora's common output. [225]
Despite uncertainty from some scholastic leaders following Sora's public demonstration, notable entertainment-industry figures have shown significant interest in the technology's potential. In an interview, actor/filmmaker Tyler Perry expressed his awe at the technology's capability to produce reasonable video from text descriptions, citing its prospective to revolutionize storytelling and content creation. He said that his excitement about Sora's possibilities was so strong that he had actually decided to pause prepare for broadening 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 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 produce tunes with 10 instruments in 15 styles. According to The Verge, a tune produced by MuseNet tends to start fairly however then fall under chaos the longer it plays. [230] [231] In pop culture, initial applications of this tool were used as early as 2020 for the web mental thriller Ben Drowned to produce 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 genre, artist, pediascape.science and a snippet of lyrics and outputs tune samples. OpenAI mentioned the tunes "show regional musical coherence [and] follow traditional chord patterns" but acknowledged that the tunes do not have "familiar larger musical structures such as choruses that repeat" and that "there is a considerable gap" in between Jukebox and human-generated music. The Verge mentioned "It's technologically remarkable, even if the outcomes seem like mushy variations of songs that might feel familiar", while Business Insider specified "surprisingly, some of the resulting tunes are catchy and sound legitimate". [234] [235] [236]
Interface

Debate Game

In 2018, OpenAI released the Debate Game, which teaches devices to discuss toy problems in front of a human judge. The purpose is to research whether such an approach 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 substantial layer and nerve cell of eight neural network models which are frequently studied in interpretability. [240] Microscope was produced to analyze the functions that form inside these neural networks easily. The designs consisted of are AlexNet, VGG-19, different 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 user interface that enables users to ask concerns in natural language. The system then responds with an answer within seconds.